About us
Gimlet is building the first multi-silicon neocloud designed for fast, efficient AI inference.
We combine large-scale compute infrastructure with an execution platform that partitions AI workloads and maps each stage to the hardware best suited to run it.
We work with foundation labs, hyperscalers, and AI-native companies, giving our team access to technical problems spanning frontier models, production infrastructure, and emerging hardware.
About the role
As a Member of Technical Staff, Compiler, you will build Gimlet's AI Compiler Stack: the layer that determines how AI workloads are represented, optimized, and executed across hardware with fundamentally different architectures, memory systems, and performance characteristics.
The compiler is one of the hardest and most critical parts of our inference stack. Its decisions reach directly into scheduling, communication, memory movement, kernel execution, and end-to-end serving performance of Gimlet’s inference stack. A compiler built for a single target can bake in assumptions about the memory hierarchy and execution model. We can't. That constraint is what makes the work interesting.
Our stack is built on open-source MLIR and LLVM compiler infrastructure. We design our own dialects, passes, and lowering paths on top of that infrastructure rather than maintaining a proprietary compiler from scratch. You will be working in an ecosystem you can carry with you, alongside people who know it deeply.
What you will work on:
Scaling the compiler architecture to a growing set of heterogeneous compute architectures. New CPUs, GPUs, and accelerators arrive with different memory models, execution models, and toolchains, and each one can't cost us a rewrite. You will work on the abstractions, dialects, and lowering paths that let the stack absorb a new target quickly and still generate code competitive with a vendor-native toolchain. Our architecture for running AI workloads across heterogeneous hardware describes where the compiler sits in the broader system.
Optimizing end-to-end performance that includes implementing operator fusion, target-agnostic middle-end optimizations, target-specific optimizations and the execution-planning decisions that shape how computation is partitioned across devices and how intermediate state moves between them. This also includes evaluating and integrating agent-driven kernel generation as a first-class code generation strategy: deciding when a generated kernel beats a library or hand-written one, and how the compiler makes that call automatically and safely. We have published early results on AI-generated CUDA kernels and AI-generated Metal kernels; folding this into the compiler itself is open ground.
Supporting new models and serving techniques in production. New architectures and inference strategies should run efficiently without per-target hand-tuning. Our work on low-latency speculative decoding and prefill/decode disaggregation across accelerators shows the shape of these problems end to end.
Who you will work with
This is a small team where the boundaries between disciplines are thin. You will be part of the compiler team and will work directly with engineers and researchers working on LLM architecture, inference optimization, kernel engineering, distributed systems, and datacenter infrastructure deciding what gets served and on what hardware. Compiler decisions here are made with full visibility into the workload and the machine, which is rarer than it sounds.
What success looks like
In your first 12–18 months, you will:
Ship compiler optimizations that measurably improve production latency, throughput, utilization, or cost.
Help bring a new accelerator architecture into production.
Reduce the engineering work required to support additional hardware targets.
Design execution strategies for partitioning workloads across heterogeneous hardware.
Extend how Gimlet selects between generated, library, and hand-written kernels.
Enable a new model architecture or serving technique across multiple targets.
You may be a good fit if you have
Experience building ML compilers and runtimes with sharp focus on performance optimizations.
Experience with MLIR, LLVM, or comparable compiler infrastructure.
Experience designing intermediate representations, writing compiler passes, or implementing lowering and code-generation pipelines.
Strong C++ and/or Python skills.
A strong understanding of SSA, memory systems, scheduling, and hardware efficiency.
Experience with operator fusion, tiling, layout transformations, or memory planning.
Experience using profilers to investigate performance across software and hardware boundaries.
Experience with IREE, XLA, TVM, Triton, or similar ML compilers and programming tools.
Strong candidates may also have
Experience optimizing ML inference or model-serving workloads.
Familiarity with kernel dispatch, launch APIs, memory allocators, or runtime interfaces.
Experience across GPU and non-GPU accelerator architectures.
Experience with automated kernel generation, autotuning, or search-based optimization.
Contributions to open-source compiler infrastructure.
Why join now?
Gimlet is expanding from its core technology into a production neocloud spanning new hardware, customers, and data centers.
Solve hard problems.
Own meaningful work.
Build for production.
Help define what’s next.
Agency Policy: Gimlet Labs does not accept unsolicited resumes from recruitment agencies or search firms. Any unsolicited resumes submitted without a signed agreement will be considered the property of Gimlet Labs, and no fees will be paid.
","descriptionPlain":"About us\n\nGimlet is building the first multi-silicon neocloud designed for fast, efficient AI inference.\n\nWe combine large-scale compute infrastructure with an execution platform that partitions AI workloads and maps each stage to the hardware best suited to run it.\n\nWe work with foundation labs, hyperscalers, and AI-native companies, giving our team access to technical problems spanning frontier models, production infrastructure, and emerging hardware.\n\nAbout the role\n\nAs a Member of Technical Staff, Compiler, you will build Gimlet's AI Compiler Stack: the layer that determines how AI workloads are represented, optimized, and executed across hardware with fundamentally different architectures, memory systems, and performance characteristics.\n\nThe compiler is one of the hardest and most critical parts of our inference stack. Its decisions reach directly into scheduling, communication, memory movement, kernel execution, and end-to-end serving performance of Gimlet’s inference stack. A compiler built for a single target can bake in assumptions about the memory hierarchy and execution model. We can't. That constraint is what makes the work interesting.\n\nOur stack is built on open-source MLIR and LLVM compiler infrastructure. We design our own dialects, passes, and lowering paths on top of that infrastructure rather than maintaining a proprietary compiler from scratch. You will be working in an ecosystem you can carry with you, alongside people who know it deeply.\n\nWhat you will work on:\n\n - Scaling the compiler architecture to a growing set of heterogeneous compute architectures. New CPUs, GPUs, and accelerators arrive with different memory models, execution models, and toolchains, and each one can't cost us a rewrite. You will work on the abstractions, dialects, and lowering paths that let the stack absorb a new target quickly and still generate code competitive with a vendor-native toolchain. Our architecture for running AI workloads across heterogeneous hardware https://gimletlabs.ai/blog/heterogeneous-ai-infrastructure describes where the compiler sits in the broader system.\n\n - Optimizing end-to-end performance that includes implementing operator fusion, target-agnostic middle-end optimizations, target-specific optimizations and the execution-planning decisions that shape how computation is partitioned across devices and how intermediate state moves between them. This also includes evaluating and integrating agent-driven kernel generation as a first-class code generation strategy: deciding when a generated kernel beats a library or hand-written one, and how the compiler makes that call automatically and safely. We have published early results on AI-generated CUDA kernels https://gimletlabs.ai/blog/ai-generated-cuda-kernels and AI-generated Metal kernels https://gimletlabs.ai/blog/ai-generated-metal-kernels; folding this into the compiler itself is open ground.\n\n - Supporting new models and serving techniques in production. New architectures and inference strategies should run efficiently without per-target hand-tuning. Our work on low-latency speculative decoding https://gimletlabs.ai/blog/low-latency-spec-decode-corsair and prefill/decode disaggregation across accelerators https://gimletlabs.ai/blog/multivendor-prefill-decode-disaggregation shows the shape of these problems end to end.\n\nWho you will work with\n\nThis is a small team where the boundaries between disciplines are thin. You will be part of the compiler team and will work directly with engineers and researchers working on LLM architecture, inference optimization, kernel engineering, distributed systems, and datacenter infrastructure deciding what gets served and on what hardware. Compiler decisions here are made with full visibility into the workload and the machine, which is rarer than it sounds.\n\nWhat success looks like\n\nIn your first 12–18 months, you will:\n\n - Ship compiler optimizations that measurably improve production latency, throughput, utilization, or cost.\n\n - Help bring a new accelerator architecture into production.\n\n - Reduce the engineering work required to support additional hardware targets.\n\n - Design execution strategies for partitioning workloads across heterogeneous hardware.\n\n - Extend how Gimlet selects between generated, library, and hand-written kernels.\n\n - Enable a new model architecture or serving technique across multiple targets.\n\nYou may be a good fit if you have\n\n - Experience building ML compilers and runtimes with sharp focus on performance optimizations.\n\n - Experience with MLIR, LLVM, or comparable compiler infrastructure.\n\n - Experience designing intermediate representations, writing compiler passes, or implementing lowering and code-generation pipelines.\n\n - Strong C++ and/or Python skills.\n\n - A strong understanding of SSA, memory systems, scheduling, and hardware efficiency.\n\n - Experience with operator fusion, tiling, layout transformations, or memory planning.\n\n - Experience using profilers to investigate performance across software and hardware boundaries.\n\n - Experience with IREE, XLA, TVM, Triton, or similar ML compilers and programming tools.\n\nStrong candidates may also have\n\n - Experience optimizing ML inference or model-serving workloads.\n\n - Familiarity with kernel dispatch, launch APIs, memory allocators, or runtime interfaces.\n\n - Experience across GPU and non-GPU accelerator architectures.\n\n - Experience with automated kernel generation, autotuning, or search-based optimization.\n\n - Contributions to open-source compiler infrastructure.\n\n\n\n\nWhy join now?\n\nGimlet is expanding from its core technology into a production neocloud spanning new hardware, customers, and data centers.\n\n - Solve hard problems.\n\n - Own meaningful work.\n\n - Build for production.\n\n - Help define what’s next.\n\nAgency Policy: Gimlet Labs does not accept unsolicited resumes from recruitment agencies or search firms. Any unsolicited resumes submitted without a signed agreement will be considered the property of Gimlet Labs, and no fees will be paid."},{"id":"5e313d06-240e-44ef-87d2-318edf246f1f","title":"Member of Technical Staff - Distributed Systems","department":"Research & Development","team":"Engineering","employmentType":"FullTime","location":"San Francisco, CA","secondaryLocations":[],"publishedAt":"2026-03-10T04:41:59.056+00:00","isListed":true,"isRemote":false,"workplaceType":"OnSite","address":{"postalAddress":{"postalCode":"94103","addressRegion":"California","streetAddress":"255 Portrero Ave","addressCountry":"United States","addressLocality":"San Francisco"}},"jobUrl":"https://jobs.ashbyhq.com/gimlet/5e313d06-240e-44ef-87d2-318edf246f1f","applyUrl":"https://jobs.ashbyhq.com/gimlet/5e313d06-240e-44ef-87d2-318edf246f1f/application","descriptionHtml":"About us
Gimlet is building the first multi-silicon neocloud designed for fast, efficient AI inference.
We combine large-scale compute infrastructure with an execution platform that partitions AI workloads and maps each stage to the hardware best suited to run it.
We work with foundation labs, hyperscalers, and AI-native companies, giving our team access to technical problems spanning frontier models, production infrastructure, and emerging hardware.
About the role
As a Member of Technical Staff, you will build the systems that schedule, route, and coordinate AI workloads across Gimlet’s infrastructure.
Different stages of an inference pipeline may run on different hardware, scale independently, and exchange state across the system. Your work will determine how those workloads are placed, coordinated, routed, recovered, and operated in production.
You will work across scheduling, orchestration, control planes, APIs, and fault tolerance. You will design systems that make distributed infrastructure easier to operate, enable workloads to run reliably across a heterogeneous fleet, and partner with compiler, ML systems, networking, and infrastructure engineers to connect the full execution stack.
What success looks like
In the first 12-18 months, you will:
Build scheduling and orchestration systems for heterogeneous compute
Design systems that manage independently scalable stages of distributed inference pipelines
Improve the reliability and fault tolerance of production AI infrastructure
Develop control planes and APIs that simplify how workloads are deployed and managed
Improve resource management and scheduling as Gimlet expands across new accelerator types, nodes, and data centers
Help the platform scale across additional hardware, nodes, and data centers
You may be a good fit if you have
Experience building or operating distributed systems in production
Strong software-engineering and systems fundamentals
The ability to reason about concurrency, consistency, failure modes, and system tradeoffs
Experience with scheduling, resource management, RPC, or asynchronous messaging
A bachelor’s degree in a relevant field or equivalent practical experience
Strong candidates may also have
Experience with Kubernetes or Kubernetes-adjacent systems beyond basic usage
Experience designing service-oriented architectures using RPC or asynchronous messaging
Familiarity with scheduling, queues, or resource management systems
Experience building reliable APIs and operating systems under high load
Software development experience in languages commonly used for systems development (e.g., Go, C++, Python)
Why join now?
Gimlet is expanding from its core technology into a production neocloud spanning new hardware, customers, and data centers.
Solve hard problems.
Own meaningful work.
Build for production.
Help define what’s next.
Agency Policy: Gimlet Labs does not accept unsolicited resumes from recruitment agencies or search firms. Any unsolicited resumes submitted without a signed agreement will be considered the property of Gimlet Labs, and no fees will be paid.
","descriptionPlain":"About us\n\nGimlet is building the first multi-silicon neocloud designed for fast, efficient AI inference.\n\nWe combine large-scale compute infrastructure with an execution platform that partitions AI workloads and maps each stage to the hardware best suited to run it.\n\nWe work with foundation labs, hyperscalers, and AI-native companies, giving our team access to technical problems spanning frontier models, production infrastructure, and emerging hardware.\n\nAbout the role\n\nAs a Member of Technical Staff, you will build the systems that schedule, route, and coordinate AI workloads across Gimlet’s infrastructure.\n\nDifferent stages of an inference pipeline may run on different hardware, scale independently, and exchange state across the system. Your work will determine how those workloads are placed, coordinated, routed, recovered, and operated in production.\n\nYou will work across scheduling, orchestration, control planes, APIs, and fault tolerance. You will design systems that make distributed infrastructure easier to operate, enable workloads to run reliably across a heterogeneous fleet, and partner with compiler, ML systems, networking, and infrastructure engineers to connect the full execution stack.\n\nWhat success looks like\n\nIn the first 12-18 months, you will:\n\n - Build scheduling and orchestration systems for heterogeneous compute\n\n - Design systems that manage independently scalable stages of distributed inference pipelines\n\n - Improve the reliability and fault tolerance of production AI infrastructure\n\n - Develop control planes and APIs that simplify how workloads are deployed and managed\n\n - Improve resource management and scheduling as Gimlet expands across new accelerator types, nodes, and data centers\n\n - Help the platform scale across additional hardware, nodes, and data centers\n\nYou may be a good fit if you have\n\n - Experience building or operating distributed systems in production\n\n - Strong software-engineering and systems fundamentals\n\n - The ability to reason about concurrency, consistency, failure modes, and system tradeoffs\n\n - Experience with scheduling, resource management, RPC, or asynchronous messaging\n\n - A bachelor’s degree in a relevant field or equivalent practical experience\n\nStrong candidates may also have\n\n - Experience with Kubernetes or Kubernetes-adjacent systems beyond basic usage\n\n - Experience designing service-oriented architectures using RPC or asynchronous messaging\n\n - Familiarity with scheduling, queues, or resource management systems\n\n - Experience building reliable APIs and operating systems under high load\n\n - Software development experience in languages commonly used for systems development (e.g., Go, C++, Python)\n\nWhy join now?\n\nGimlet is expanding from its core technology into a production neocloud spanning new hardware, customers, and data centers.\n\n - Solve hard problems.\n\n - Own meaningful work.\n\n - Build for production.\n\n - Help define what’s next.\n\nAgency Policy: Gimlet Labs does not accept unsolicited resumes from recruitment agencies or search firms. Any unsolicited resumes submitted without a signed agreement will be considered the property of Gimlet Labs, and no fees will be paid."},{"id":"376f7e48-4f3f-48bc-a59f-16529364a32c","title":"Member of Technical Staff - Kernels & GPU Performance","department":"Research & Development","team":"Engineering","employmentType":"FullTime","location":"San Francisco, CA","secondaryLocations":[],"publishedAt":"2026-03-10T04:43:28.158+00:00","isListed":true,"isRemote":false,"workplaceType":"OnSite","address":{"postalAddress":{"postalCode":"94103","addressRegion":"California","streetAddress":"255 Portrero Ave","addressCountry":"United States","addressLocality":"San Francisco"}},"jobUrl":"https://jobs.ashbyhq.com/gimlet/376f7e48-4f3f-48bc-a59f-16529364a32c","applyUrl":"https://jobs.ashbyhq.com/gimlet/376f7e48-4f3f-48bc-a59f-16529364a32c/application","descriptionHtml":"About us
Gimlet is building the first multi-silicon neocloud designed for fast, efficient AI inference.
We combine large-scale compute infrastructure with an execution platform that partitions AI workloads and maps each stage to the hardware best suited to run it.
We work with foundation labs, hyperscalers, and AI-native companies, giving our team access to technical problems spanning frontier models, production infrastructure, and emerging hardware.
About the role
As a Member of Technical Staff, you will build and optimize the low-level execution primitives that turn accelerator performance into production inference performance.
Rather than optimizing for one hardware architecture, you will work across accelerators with different execution models, memory hierarchies, capabilities, and software stacks. Your work will shape the latency, throughput, and efficiency Gimlet can achieve across established and emerging hardware architectures.
You will work close to the hardware across kernel implementation, memory access, execution behavior, profiling, and performance validation. You will develop optimizations that account for differences between accelerator architectures and partner with compiler, ML systems, and distributed systems engineers to improve performance across the full execution stack.
What success looks like
In the first 12-18 months, you will:
Build and optimize kernels that improve latency, throughput, and hardware utilization for production AI workloads
Develop execution strategies that unlock performance across both established and emerging accelerator architectures
Improve memory efficiency, scheduling behavior, and execution characteristics across the inference stack
Partner with compiler, runtime, and distributed systems engineers to ensure end-to-end performance optimization
Influence how heterogeneous hardware is deployed and utilized within the next generation of AI infrastructure
Help establish performance engineering standards that shape the future of Gimlet's execution platform
You may be a good fit if you have
Strong software engineering fundamentals
Experience working on performance-critical systems close to hardware
Comfort reasoning about low-level execution behavior, memory hierarchies, and performance tradeoffs
Bachelor's degree in a relevant field, or an equivalent combination of education, training, and professional experience.
Strong candidates may also have
Experience with CUDA, Triton, CUTLASS, or other accelerator programming models
Deep understanding of GPU execution models (warps/wavefronts, blocks, grids)
Experience optimizing memory access patterns (coalescing, shared memory, cache behavior)
Familiarity with occupancy, latency hiding, and instruction-level parallelism
Experience using profiling and performance analysis tools
Familiarity with multi-GPU or distributed execution is a plus
Why join now?
Gimlet is expanding from its core technology into a production neocloud spanning new hardware, customers, and data centers.
Solve hard problems.
Own meaningful work.
Build for production.
Help define what’s next.
Agency Policy: Gimlet Labs does not accept unsolicited resumes from recruitment agencies or search firms. Any unsolicited resumes submitted without a signed agreement will be considered the property of Gimlet Labs, and no fees will be paid.
","descriptionPlain":"About us\n\nGimlet is building the first multi-silicon neocloud designed for fast, efficient AI inference.\n\nWe combine large-scale compute infrastructure with an execution platform that partitions AI workloads and maps each stage to the hardware best suited to run it.\n\nWe work with foundation labs, hyperscalers, and AI-native companies, giving our team access to technical problems spanning frontier models, production infrastructure, and emerging hardware.\n\nAbout the role\n\nAs a Member of Technical Staff, you will build and optimize the low-level execution primitives that turn accelerator performance into production inference performance.\n\nRather than optimizing for one hardware architecture, you will work across accelerators with different execution models, memory hierarchies, capabilities, and software stacks. Your work will shape the latency, throughput, and efficiency Gimlet can achieve across established and emerging hardware architectures.\n\nYou will work close to the hardware across kernel implementation, memory access, execution behavior, profiling, and performance validation. You will develop optimizations that account for differences between accelerator architectures and partner with compiler, ML systems, and distributed systems engineers to improve performance across the full execution stack.\n\nWhat success looks like\n\nIn the first 12-18 months, you will:\n\n - Build and optimize kernels that improve latency, throughput, and hardware utilization for production AI workloads\n\n - Develop execution strategies that unlock performance across both established and emerging accelerator architectures\n\n - Improve memory efficiency, scheduling behavior, and execution characteristics across the inference stack\n\n - Partner with compiler, runtime, and distributed systems engineers to ensure end-to-end performance optimization\n\n - Influence how heterogeneous hardware is deployed and utilized within the next generation of AI infrastructure\n\n - Help establish performance engineering standards that shape the future of Gimlet's execution platform\n\nYou may be a good fit if you have\n\n - Strong software engineering fundamentals\n\n - Experience working on performance-critical systems close to hardware\n\n - Comfort reasoning about low-level execution behavior, memory hierarchies, and performance tradeoffs\n\n - Bachelor's degree in a relevant field, or an equivalent combination of education, training, and professional experience.\n\nStrong candidates may also have\n\n - Experience with CUDA, Triton, CUTLASS, or other accelerator programming models\n\n - Deep understanding of GPU execution models (warps/wavefronts, blocks, grids)\n\n - Experience optimizing memory access patterns (coalescing, shared memory, cache behavior)\n\n - Familiarity with occupancy, latency hiding, and instruction-level parallelism\n\n - Experience using profiling and performance analysis tools\n\n - Familiarity with multi-GPU or distributed execution is a plus\n\nWhy join now?\n\nGimlet is expanding from its core technology into a production neocloud spanning new hardware, customers, and data centers.\n\n - Solve hard problems.\n\n - Own meaningful work.\n\n - Build for production.\n\n - Help define what’s next.\n\nAgency Policy: Gimlet Labs does not accept unsolicited resumes from recruitment agencies or search firms. Any unsolicited resumes submitted without a signed agreement will be considered the property of Gimlet Labs, and no fees will be paid."},{"id":"8df5a18f-fb87-4c23-8b8b-efffdace4223","title":"Member of Technical Staff - ML Systems & Inference","department":"Research & Development","team":"Engineering","employmentType":"FullTime","location":"San Francisco, CA","secondaryLocations":[],"publishedAt":"2026-03-10T04:46:01.368+00:00","isListed":true,"isRemote":false,"workplaceType":"OnSite","address":{"postalAddress":{"postalCode":"94103","addressRegion":"California","streetAddress":"255 Portrero Ave","addressCountry":"United States","addressLocality":"San Francisco"}},"jobUrl":"https://jobs.ashbyhq.com/gimlet/8df5a18f-fb87-4c23-8b8b-efffdace4223","applyUrl":"https://jobs.ashbyhq.com/gimlet/8df5a18f-fb87-4c23-8b8b-efffdace4223/application","descriptionHtml":"About us
Gimlet is building the first multi-silicon neocloud designed for fast, efficient AI inference.
We combine large-scale compute infrastructure with an execution platform that partitions AI workloads and maps each stage to the hardware best suited to run it.
We work with foundation labs, hyperscalers, and AI-native companies, giving our team access to technical problems spanning frontier models, production infrastructure, and emerging hardware.
About the role
As a Member of Technical Staff focused on ML Systems, you will build the inference systems that execute models end-to-end in production.
You will work on the systems that determine how inference executes across that pipeline: how requests are batched and scheduled, how stages are placed and scaled, how KV cache and intermediate state move between accelerators, and how the system balances latency, throughput, and utilization across different hardware characteristics.
You will work across model serving, batching, scheduling, concurrency, KV cache management, and memory placement. You will help bring up models on novel hardware. You will support new model architectures and inference techniques, improve performance under real production workloads, and partner with compiler, kernel, networking, and distributed systems engineers to optimize the full execution path.
What success looks like
In the first 12-18 months, you will:
Improve the latency, throughput, and efficiency of production inference workloads
Design execution strategies across batching, scheduling, concurrency, and resource utilization
Improve KV cache management, memory efficiency, and execution under load
Enable new models, accelerator architectures, and inference techniques to run efficiently in production
You may be a good fit if you have
Strong software engineering fundamentals
Experience building or operating ML inference or model serving systems
Comfort reasoning about performance, memory usage, and system behavior under load
Bachelor's degree in a relevant field, or an equivalent combination of education, training, and professional experience.
Strong candidates may also have
Experience with inference runtimes such as TensorRT-LLM, vLLM, or custom serving systems
Deep understanding of modern model architectures and attention mechanisms
Experience with batching, scheduling, and concurrency control in inference systems
Familiarity with KV cache management and memory placement strategies
Experience profiling and tuning latency- and throughput-critical systems
Software development experience in Python and C++
Why join now?
Gimlet is expanding from its core technology into a production neocloud spanning new hardware, customers, and data centers.
Solve hard problems.
Own meaningful work.
Build for production.
Help define what’s next.
Agency Policy: Gimlet Labs does not accept unsolicited resumes from recruitment agencies or search firms. Any unsolicited resumes submitted without a signed agreement will be considered the property of Gimlet Labs, and no fees will be paid.
","descriptionPlain":"About us\n\nGimlet is building the first multi-silicon neocloud designed for fast, efficient AI inference.\n\nWe combine large-scale compute infrastructure with an execution platform that partitions AI workloads and maps each stage to the hardware best suited to run it.\n\nWe work with foundation labs, hyperscalers, and AI-native companies, giving our team access to technical problems spanning frontier models, production infrastructure, and emerging hardware.\n\nAbout the role\n\nAs a Member of Technical Staff focused on ML Systems, you will build the inference systems that execute models end-to-end in production.\n\nYou will work on the systems that determine how inference executes across that pipeline: how requests are batched and scheduled, how stages are placed and scaled, how KV cache and intermediate state move between accelerators, and how the system balances latency, throughput, and utilization across different hardware characteristics.\n\nYou will work across model serving, batching, scheduling, concurrency, KV cache management, and memory placement. You will help bring up models on novel hardware. You will support new model architectures and inference techniques, improve performance under real production workloads, and partner with compiler, kernel, networking, and distributed systems engineers to optimize the full execution path.\n\nWhat success looks like\n\nIn the first 12-18 months, you will:\n\n - Improve the latency, throughput, and efficiency of production inference workloads\n\n - Design execution strategies across batching, scheduling, concurrency, and resource utilization\n\n - Improve KV cache management, memory efficiency, and execution under load\n\n - Enable new models, accelerator architectures, and inference techniques to run efficiently in production\n\nYou may be a good fit if you have\n\n - Strong software engineering fundamentals\n\n - Experience building or operating ML inference or model serving systems\n\n - Comfort reasoning about performance, memory usage, and system behavior under load\n\n - Bachelor's degree in a relevant field, or an equivalent combination of education, training, and professional experience.\n\nStrong candidates may also have\n\n - Experience with inference runtimes such as TensorRT-LLM, vLLM, or custom serving systems\n\n - Deep understanding of modern model architectures and attention mechanisms\n\n - Experience with batching, scheduling, and concurrency control in inference systems\n\n - Familiarity with KV cache management and memory placement strategies\n\n - Experience profiling and tuning latency- and throughput-critical systems\n\n - Software development experience in Python and C++\n\nWhy join now?\n\nGimlet is expanding from its core technology into a production neocloud spanning new hardware, customers, and data centers.\n\n - Solve hard problems.\n\n - Own meaningful work.\n\n - Build for production.\n\n - Help define what’s next.\n\nAgency Policy: Gimlet Labs does not accept unsolicited resumes from recruitment agencies or search firms. Any unsolicited resumes submitted without a signed agreement will be considered the property of Gimlet Labs, and no fees will be paid."},{"id":"9aebe281-68e8-4610-a33a-fe5a45e45ad7","title":"Network Engineer ","department":"Research & Development","team":"Engineering","employmentType":"FullTime","location":"San Francisco, CA","secondaryLocations":[],"publishedAt":"2026-06-01T18:36:14.102+00:00","isListed":true,"isRemote":false,"workplaceType":"OnSite","address":{"postalAddress":{"postalCode":"94103","addressRegion":"California","streetAddress":"255 Portrero Ave","addressCountry":"United States","addressLocality":"San Francisco"}},"jobUrl":"https://jobs.ashbyhq.com/gimlet/9aebe281-68e8-4610-a33a-fe5a45e45ad7","applyUrl":"https://jobs.ashbyhq.com/gimlet/9aebe281-68e8-4610-a33a-fe5a45e45ad7/application","descriptionHtml":"About us
Gimlet is building the first multi-silicon neocloud designed for fast, efficient AI inference.
We combine large-scale compute infrastructure with an execution platform that partitions AI workloads and maps each stage to the hardware best suited to run it.
We work with foundation labs, hyperscalers, and AI-native companies, giving our team access to technical problems spanning frontier models, production infrastructure, and emerging hardware.
About the role
As a Network Engineer, you will build the network infrastructure connecting Gimlet’s compute, storage, and distributed systems. Your work will shape how our data centers are designed, deployed, and operated as the platform scales.
You will work across network architecture, physical infrastructure, provisioning, deployment, and production operations. You will design high-performance network fabrics, bring new sites and hardware online, automate operational workflows, and troubleshoot performance and reliability issues across network and systems boundaries.
What success looks like
In the first 12-18 months, you will:
Design and deploy network infrastructure for new data centers and hardware expansions
Improve network performance and reliability for distributed AI workloads
Automate provisioning, configuration, validation, and monitoring workflows
Establish repeatable network standards that make infrastructure deployments faster and more reliable
Troubleshoot complex issues across network, hardware, and distributed systems boundaries
You may be a good fit if you have
Experience designing, deploying, and operating production data center networks
Strong fundamentals in routing, switching, performance, and reliability
Experience with BGP, ECMP, EVPN, VXLAN, and spine-leaf or Clos fabrics
Knowledge of high-performance networking technologies such as RDMA, RoCE, InfiniBand, or lossless Ethernet
The ability to troubleshoot across network, hardware, software, and distributed systems boundaries
Experience automating network infrastructure with Python, Ansible, Terraform, or similar tools
Strong candidates may also have
Experience with AI/HPC, GPU, or large-scale distributed infrastructure.
Knowledge of AI application traffic patterns, including collective operations and workload colocation strategies that optimize network performance.
Experience with cloud networking on GCP, AWS, Azure, or similar platforms.
Experience with Arista, Cisco, Juniper, or NVIDIA networking platforms, as well as Palo Alto Networks PAN-OS.
Experience with network automation using Python, Ansible, Terraform, or similar tooling.
Familiarity with RDMA, RoCE, InfiniBand, or other high-performance networking environments.
Why join now?
Gimlet is expanding from its core technology into a production neocloud spanning new hardware, customers, and data centers.
Solve hard problems.
Own meaningful work.
Build for production.
Help define what’s next.
Agency Policy: Gimlet Labs does not accept unsolicited resumes from recruitment agencies or search firms. Any unsolicited resumes submitted without a signed agreement will be considered the property of Gimlet Labs, and no fees will be paid.
","descriptionPlain":"About us\n\nGimlet is building the first multi-silicon neocloud designed for fast, efficient AI inference.\n\nWe combine large-scale compute infrastructure with an execution platform that partitions AI workloads and maps each stage to the hardware best suited to run it.\n\nWe work with foundation labs, hyperscalers, and AI-native companies, giving our team access to technical problems spanning frontier models, production infrastructure, and emerging hardware.\n\nAbout the role\n\nAs a Network Engineer, you will build the network infrastructure connecting Gimlet’s compute, storage, and distributed systems. Your work will shape how our data centers are designed, deployed, and operated as the platform scales.\n\nYou will work across network architecture, physical infrastructure, provisioning, deployment, and production operations. You will design high-performance network fabrics, bring new sites and hardware online, automate operational workflows, and troubleshoot performance and reliability issues across network and systems boundaries.\n\nWhat success looks like\n\nIn the first 12-18 months, you will:\n\n - Design and deploy network infrastructure for new data centers and hardware expansions\n\n - Improve network performance and reliability for distributed AI workloads\n\n - Automate provisioning, configuration, validation, and monitoring workflows\n\n - Establish repeatable network standards that make infrastructure deployments faster and more reliable\n\n - Troubleshoot complex issues across network, hardware, and distributed systems boundaries\n\nYou may be a good fit if you have\n\n - Experience designing, deploying, and operating production data center networks\n\n - Strong fundamentals in routing, switching, performance, and reliability\n\n - Experience with BGP, ECMP, EVPN, VXLAN, and spine-leaf or Clos fabrics\n\n - Knowledge of high-performance networking technologies such as RDMA, RoCE, InfiniBand, or lossless Ethernet\n\n - The ability to troubleshoot across network, hardware, software, and distributed systems boundaries\n\n - Experience automating network infrastructure with Python, Ansible, Terraform, or similar tools\n\nStrong candidates may also have \n\n - Experience with AI/HPC, GPU, or large-scale distributed infrastructure.\n\n - Knowledge of AI application traffic patterns, including collective operations and workload colocation strategies that optimize network performance.\n\n - Experience with cloud networking on GCP, AWS, Azure, or similar platforms.\n\n - Experience with Arista, Cisco, Juniper, or NVIDIA networking platforms, as well as Palo Alto Networks PAN-OS.\n\n - Experience with network automation using Python, Ansible, Terraform, or similar tooling.\n\n - Familiarity with RDMA, RoCE, InfiniBand, or other high-performance networking environments.\n\nWhy join now?\n\nGimlet is expanding from its core technology into a production neocloud spanning new hardware, customers, and data centers.\n\n - Solve hard problems.\n\n - Own meaningful work.\n\n - Build for production.\n\n - Help define what’s next.\n\nAgency Policy: Gimlet Labs does not accept unsolicited resumes from recruitment agencies or search firms. Any unsolicited resumes submitted without a signed agreement will be considered the property of Gimlet Labs, and no fees will be paid."},{"id":"187f53a3-a1d2-4704-a866-f32817390e07","title":"Member of Technical Staff - Infrastructure","department":"Research & Development","team":"Engineering","employmentType":"FullTime","location":"San Francisco, CA","secondaryLocations":[],"publishedAt":"2026-06-12T22:41:11.715+00:00","isListed":true,"isRemote":false,"workplaceType":"OnSite","address":{"postalAddress":{"postalCode":"94103","addressRegion":"California","streetAddress":"255 Portrero Ave","addressCountry":"United States","addressLocality":"San Francisco"}},"jobUrl":"https://jobs.ashbyhq.com/gimlet/187f53a3-a1d2-4704-a866-f32817390e07","applyUrl":"https://jobs.ashbyhq.com/gimlet/187f53a3-a1d2-4704-a866-f32817390e07/application","descriptionHtml":"About us
Gimlet is building the first multi-silicon neocloud designed for fast, efficient AI inference.
We combine large-scale compute infrastructure with an execution platform that partitions AI workloads and maps each stage to the hardware best suited to run it.
We work with foundation labs, hyperscalers, and AI-native companies, giving our team access to technical problems spanning frontier models, production infrastructure, and emerging hardware.
About the role
As an Infrastructure Platform Engineer, you will build the systems that turn heterogeneous accelerator hardware into reliable production infrastructure for Gimlet's AI cloud.
Gimlet's fleet spans hardware with different architectures, software stacks, operational characteristics, and failure modes. Your work will determine how new hardware is brought online, how clusters are provisioned and operated, and how production inference systems remain reliable as the fleet scales.
You will work across bare metal, Linux, Kubernetes, cluster scheduling, observability, and automation. You will build systems that abstract differences between accelerator architectures, make new hardware production-ready, and improve the reliability and operability of Gimlet’s infrastructure.
What success looks like
In your first 12–18 months, you will:
Deploy and operate production clusters across different accelerator architectures
Automate hardware provisioning, validation, upgrades, and fleet lifecycle management
Improve cluster scheduling, resource utilization, isolation, and capacity management
Build observable infrastructure that enables faster debugging, incident response, and recovery
Partner across distributed systems, runtime, compiler, networking, and hardware teams to bring new accelerators into production
You may be a good fit if you have
Experience in infrastructure, cluster engineering, platform engineering, SRE, or HPC
Strong Linux systems knowledge and production debugging experience
Experience operating Kubernetes, Slurm, Nomad, or similar orchestration systems
Experience automating infrastructure with Python, Go, Terraform, Ansible, or similar tools
Experience with GPU or accelerator infrastructure, including drivers, firmware, or CUDA/ROCm
The ability to build systems that are observable, recoverable, and reliable in production
A bachelor’s degree in a relevant field or equivalent practical experience
Strong candidates may also have
Experience building or operating AI inference, training, HPC, or neocloud infrastructure
Experience with bare-metal provisioning, PXE/iPXE, image pipelines, BIOS/firmware management, or rack bring-up
Experience with multi-tenant cluster isolation, quota systems, fair scheduling, or usage accounting
Experience debugging distributed workload performance across compute, memory, network, and storage bottlenecks
Experience building observability platforms using technologies such as Prometheus, OpenTelemetry, Grafana, or similar tooling
Familiarity with heterogeneous hardware environments across NVIDIA, AMD, Intel, ARM, or emerging accelerators
Why join now?
Gimlet is expanding from its core technology into a production neocloud spanning new hardware, customers, and data centers.
Solve hard problems.
Own meaningful work.
Build for production.
Help define what’s next.
Agency Policy: Gimlet Labs does not accept unsolicited resumes from recruitment agencies or search firms. Any unsolicited resumes submitted without a signed agreement will be considered the property of Gimlet Labs, and no fees will be paid.
","descriptionPlain":"About us\n\nGimlet is building the first multi-silicon neocloud designed for fast, efficient AI inference.\n\nWe combine large-scale compute infrastructure with an execution platform that partitions AI workloads and maps each stage to the hardware best suited to run it.\n\nWe work with foundation labs, hyperscalers, and AI-native companies, giving our team access to technical problems spanning frontier models, production infrastructure, and emerging hardware.\n\nAbout the role\n\nAs an Infrastructure Platform Engineer, you will build the systems that turn heterogeneous accelerator hardware into reliable production infrastructure for Gimlet's AI cloud.\n\nGimlet's fleet spans hardware with different architectures, software stacks, operational characteristics, and failure modes. Your work will determine how new hardware is brought online, how clusters are provisioned and operated, and how production inference systems remain reliable as the fleet scales.\n\nYou will work across bare metal, Linux, Kubernetes, cluster scheduling, observability, and automation. You will build systems that abstract differences between accelerator architectures, make new hardware production-ready, and improve the reliability and operability of Gimlet’s infrastructure.\n\nWhat success looks like\n\nIn your first 12–18 months, you will:\n\n - Deploy and operate production clusters across different accelerator architectures\n\n - Automate hardware provisioning, validation, upgrades, and fleet lifecycle management\n\n - Improve cluster scheduling, resource utilization, isolation, and capacity management\n\n - Build observable infrastructure that enables faster debugging, incident response, and recovery\n\n - Partner across distributed systems, runtime, compiler, networking, and hardware teams to bring new accelerators into production\n\nYou may be a good fit if you have\n\n - Experience in infrastructure, cluster engineering, platform engineering, SRE, or HPC\n\n - Strong Linux systems knowledge and production debugging experience\n\n - Experience operating Kubernetes, Slurm, Nomad, or similar orchestration systems\n\n - Experience automating infrastructure with Python, Go, Terraform, Ansible, or similar tools\n\n - Experience with GPU or accelerator infrastructure, including drivers, firmware, or CUDA/ROCm\n\n - The ability to build systems that are observable, recoverable, and reliable in production\n\n - A bachelor’s degree in a relevant field or equivalent practical experience\n\nStrong candidates may also have\n\n - Experience building or operating AI inference, training, HPC, or neocloud infrastructure\n\n - Experience with bare-metal provisioning, PXE/iPXE, image pipelines, BIOS/firmware management, or rack bring-up\n\n - Experience with multi-tenant cluster isolation, quota systems, fair scheduling, or usage accounting\n\n - Experience debugging distributed workload performance across compute, memory, network, and storage bottlenecks\n\n - Experience building observability platforms using technologies such as Prometheus, OpenTelemetry, Grafana, or similar tooling\n\n - Familiarity with heterogeneous hardware environments across NVIDIA, AMD, Intel, ARM, or emerging accelerators\n\nWhy join now?\n\nGimlet is expanding from its core technology into a production neocloud spanning new hardware, customers, and data centers.\n\n - Solve hard problems.\n\n - Own meaningful work.\n\n - Build for production.\n\n - Help define what’s next.\n\nAgency Policy: Gimlet Labs does not accept unsolicited resumes from recruitment agencies or search firms. Any unsolicited resumes submitted without a signed agreement will be considered the property of Gimlet Labs, and no fees will be paid."},{"id":"51600cfd-6f8d-4925-8795-3f0f730e5d57","title":"Member of Security Staff, Governance, Risk and Compliance","department":"Research & Development","team":"Engineering","employmentType":"FullTime","location":"San Francisco, CA","secondaryLocations":[],"publishedAt":"2026-06-01T18:59:29.131+00:00","isListed":true,"isRemote":false,"workplaceType":"OnSite","address":{"postalAddress":{"postalCode":"94103","addressRegion":"California","streetAddress":"255 Portrero Ave","addressCountry":"United States","addressLocality":"San Francisco"}},"jobUrl":"https://jobs.ashbyhq.com/gimlet/51600cfd-6f8d-4925-8795-3f0f730e5d57","applyUrl":"https://jobs.ashbyhq.com/gimlet/51600cfd-6f8d-4925-8795-3f0f730e5d57/application","descriptionHtml":"About us
Gimlet is building the first multi-silicon neocloud designed for fast, efficient AI inference.
We combine large-scale compute infrastructure with an execution platform that partitions AI workloads and maps each stage to the hardware best suited to run it.
We work with foundation labs, hyperscalers, and AI-native companies, giving our team access to technical problems spanning frontier models, production infrastructure, and emerging hardware.
About the role
Gimlet Labs is looking for a Member of Security Staff, Governance, Risk and Compliance to build and own the security and compliance foundation for an AI company operating across rapidly evolving AI systems serving production scale traffic for top frontier labs and hyperscalers.
This is a highly hands-on role for someone who can design the compliance program, implement the technical controls, and work directly with engineering to make security auditable, scalable, and practical. You will have significant ownership over the compliance stack, including policies, controls, evidence collection, audit readiness, vendor risk, and security tooling.
What success looks like
In the first 12-18 months, you will:
Partner directly with engineering, infrastructure, and product teams to identify security risks and design practical controls across AI platforms, cloud infrastructure, networking systems, APIs, and software delivery pipelines.
Build and operationalize security and compliance programs supporting frameworks such as SOC 2, ISO 27001, NIST CSF, NIST AI RMF, CSA CCM, and customer security requirements.
Drive improvements to cloud and application security controls, including IAM, network segmentation, encryption, logging, secrets management, vulnerability management, and secure SDLC practices.
Help define security approaches for AI systems, including model access controls, data protection, third-party AI tooling, auditability, and misuse prevention.
Build scalable processes for audit evidence collection, risk tracking, remediation management, and security reporting across technical and non-technical stakeholders.
Contribute to broader security and operational readiness efforts including vendor risk management, incident response preparedness, business continuity planning, and security policy development.
You may be a good fit if
Experience in security risk, compliance, GRC, cloud security, or infrastructure security.
Working knowledge of cloud platforms such as AWS, Azure, or Google Cloud.
Familiarity with networking concepts including firewalls, VPC/VNet design, VPNs, DNS, TLS, routing, segmentation, and zero trust principles.
Understanding of software security concepts, including secure SDLC, CI/CD, vulnerability management, secrets management, and API security.
Experience with compliance frameworks such as SOC 2, ISO 27001, NIST, CIS Controls, or CSA CCM.
Ability to document controls, gather evidence, assess gaps, and drive remediation with engineering teams.
Strong written and verbal communication skills.
Strong candidates may also have
Experience in an early-stage startup or high-ownership environment.
Experience supporting AI, machine learning, data infrastructure, or SaaS platforms.
Familiarity with AI governance frameworks such as NIST AI RMF or ISO/IEC 42001.
Experience with Kubernetes, containers, infrastructure as code, and cloud-native security tooling.
Certifications such as CISSP, CISA, CRISC, CCSP, CCSK, Security+, AWS Security Specialty, or Azure Security Engineer.
Experience implementing or administering GRC platforms, SIEMs, CSPM tools, vulnerability scanners, and ticketing workflows.
Why join now?
Gimlet is expanding from its core technology into a production neocloud spanning new hardware, customers, and data centers.
Solve hard problems.
Own meaningful work.
Build for production.
Help define what’s next.
Agency Policy: Gimlet Labs does not accept unsolicited resumes from recruitment agencies or search firms. Any unsolicited resumes submitted without a signed agreement will be considered the property of Gimlet Labs, and no fees will be paid.
","descriptionPlain":"About us\n\nGimlet is building the first multi-silicon neocloud designed for fast, efficient AI inference.\n\nWe combine large-scale compute infrastructure with an execution platform that partitions AI workloads and maps each stage to the hardware best suited to run it.\n\nWe work with foundation labs, hyperscalers, and AI-native companies, giving our team access to technical problems spanning frontier models, production infrastructure, and emerging hardware.\n\nAbout the role\n\nGimlet Labs is looking for a Member of Security Staff, Governance, Risk and Compliance to build and own the security and compliance foundation for an AI company operating across rapidly evolving AI systems serving production scale traffic for top frontier labs and hyperscalers.\n\nThis is a highly hands-on role for someone who can design the compliance program, implement the technical controls, and work directly with engineering to make security auditable, scalable, and practical. You will have significant ownership over the compliance stack, including policies, controls, evidence collection, audit readiness, vendor risk, and security tooling.\n\nWhat success looks like\n\nIn the first 12-18 months, you will:\n\n - Partner directly with engineering, infrastructure, and product teams to identify security risks and design practical controls across AI platforms, cloud infrastructure, networking systems, APIs, and software delivery pipelines.\n\n - Build and operationalize security and compliance programs supporting frameworks such as SOC 2, ISO 27001, NIST CSF, NIST AI RMF, CSA CCM, and customer security requirements.\n\n - Drive improvements to cloud and application security controls, including IAM, network segmentation, encryption, logging, secrets management, vulnerability management, and secure SDLC practices.\n\n - Help define security approaches for AI systems, including model access controls, data protection, third-party AI tooling, auditability, and misuse prevention.\n\n - Build scalable processes for audit evidence collection, risk tracking, remediation management, and security reporting across technical and non-technical stakeholders.\n\n - Contribute to broader security and operational readiness efforts including vendor risk management, incident response preparedness, business continuity planning, and security policy development.\n\nYou may be a good fit if\n\n - Experience in security risk, compliance, GRC, cloud security, or infrastructure security.\n\n - Working knowledge of cloud platforms such as AWS, Azure, or Google Cloud.\n\n - Familiarity with networking concepts including firewalls, VPC/VNet design, VPNs, DNS, TLS, routing, segmentation, and zero trust principles.\n\n - Understanding of software security concepts, including secure SDLC, CI/CD, vulnerability management, secrets management, and API security.\n\n - Experience with compliance frameworks such as SOC 2, ISO 27001, NIST, CIS Controls, or CSA CCM.\n\n - Ability to document controls, gather evidence, assess gaps, and drive remediation with engineering teams.\n\n - Strong written and verbal communication skills.\n\nStrong candidates may also have\n\n - Experience in an early-stage startup or high-ownership environment.\n\n - Experience supporting AI, machine learning, data infrastructure, or SaaS platforms.\n\n - Familiarity with AI governance frameworks such as NIST AI RMF or ISO/IEC 42001.\n\n - Experience with Kubernetes, containers, infrastructure as code, and cloud-native security tooling.\n\n - Certifications such as CISSP, CISA, CRISC, CCSP, CCSK, Security+, AWS Security Specialty, or Azure Security Engineer.\n\n - Experience implementing or administering GRC platforms, SIEMs, CSPM tools, vulnerability scanners, and ticketing workflows.\n\nWhy join now?\n\nGimlet is expanding from its core technology into a production neocloud spanning new hardware, customers, and data centers.\n\n - Solve hard problems.\n\n - Own meaningful work.\n\n - Build for production.\n\n - Help define what’s next.\n\nAgency Policy: Gimlet Labs does not accept unsolicited resumes from recruitment agencies or search firms. Any unsolicited resumes submitted without a signed agreement will be considered the property of Gimlet Labs, and no fees will be paid."},{"id":"17988d93-9f1c-49b9-bd35-13e407ec46af","title":"Data Center Facilities Operations Lead","department":"Data Center & Operations","team":"DC Ops","employmentType":"FullTime","location":"San Francisco, CA","secondaryLocations":[{"location":"Remote","address":null}],"publishedAt":"2026-08-13T22:01:14.074+00:00","isListed":true,"isRemote":true,"workplaceType":"Remote","address":{"postalAddress":{"postalCode":"94103","addressRegion":"California","streetAddress":"255 Portrero Ave","addressCountry":"United States","addressLocality":"San Francisco"}},"jobUrl":"https://jobs.ashbyhq.com/gimlet/17988d93-9f1c-49b9-bd35-13e407ec46af","applyUrl":"https://jobs.ashbyhq.com/gimlet/17988d93-9f1c-49b9-bd35-13e407ec46af/application","descriptionHtml":"About us
Gimlet is building the first multi-silicon neocloud designed for fast, efficient AI inference.
We combine large-scale compute infrastructure with an execution platform that partitions AI workloads and maps each stage to the hardware best suited to run it.
We work with foundation labs, hyperscalers, and AI-native companies, giving our team access to technical problems spanning frontier models, production infrastructure, and emerging hardware.
About the role
Gimlet Labs is seeking a Data Center Facilities Operations Lead to own the critical facilities operating model for Gimlet data centers and high-density AI infrastructure deployments. In this role, you will make sure the facility-side systems that support Gimlet's compute capacity are ready, monitored, maintained, and operating inside the required envelope.
You will focus on the infrastructure that keeps liquid-cooled AI systems healthy: facility water loops, CDUs, supply and return temperatures, flow, pressure, water quality, leak detection, alarms, heat rejection, power and cooling coordination, BMS/DCIM telemetry, maintenance procedures, and vendor repair workflows.
This role is well-suited for a critical facilities operator who understands data center MEP systems, liquid cooling, operational monitoring, and the discipline required to keep high-density compute environments stable as Gimlet scales.
What success looks like
In the first 12-18 months, you will:
Build the facilities operations model for current and future Gimlet sites, including operating standards, escalation paths, maintenance routines, acceptance criteria, and facility readiness gates.
Translate OEM and engineering requirements for liquid-cooled platforms into practical site operating envelopes for temperature, flow, pressure, water quality, alarms, and heat rejection.
Own monitoring and response for facility-side telemetry, including supply and return water temperatures, delta-T, flow, pressure, leak detection, CDU status, cooling capacity margins, and BMS/DCIM alarms.
Partner with colocation providers, facility vendors, OEMs, Site Managers, Data Center Technicians, Deployment Leads, and TPMs to ensure facilities are ready before new compute capacity is deployed.
Create and maintain MOPs, SOPs, EOPs, maintenance windows, runbooks, inspection routines, and incident response procedures for critical facilities and liquid cooling operations.
Coordinate preventive maintenance, repairs, and vendor response for CDUs, facility water loops, filters, valves, pumps, sensors, leak detection systems, chillers, dry coolers, CRAHs, and related infrastructure.
Lead facility-side root cause analysis for thermal, leak, power, cooling, monitoring, and environmental events, then drive durable corrective actions.
Build reporting that shows facility health, risk, readiness, capacity margin, recurring issues, open repairs, and operational trends across Gimlet sites.
You may be a good fit if you have
Experience in data center facilities operations, critical facilities engineering, MEP operations, commissioning, or facilities maintenance
Experience operating liquid-cooled, high-density compute infrastructure
Familiarity with facility water systems, CDUs, heat rejection, leak detection, and water-quality controls
Experience using BMS, DCIM, EPMS, or similar systems to monitor and respond to facility conditions
The ability to create and execute operational procedures with strong attention to safety and reliability
Experience coordinating across site teams, colocation providers, OEMs, and facilities vendors
The ability to work in active data center environments and support urgent facilities escalations
Strong candidates may also have
Experience supporting GPU, HPC, or rack-scale liquid-cooled infrastructure
Experience with commissioning, integrated systems testing, site acceptance, or facility turnover
Familiarity with power distribution, UPS and generator systems, chilled water, dry coolers, CRAH/CRAC systems, or rear-door heat exchangers
Experience managing colocation obligations, service levels, maintenance windows, and vendor escalations
A track record of improving facility reliability through monitoring, preventive maintenance, and incident analysis
Why join now?
Gimlet is expanding from its core technology into a production neocloud spanning new hardware, customers, and data centers.
Solve hard problems.
Own meaningful work.
Build for production.
Help define what’s next.
Agency Policy: Gimlet Labs does not accept unsolicited resumes from recruitment agencies or search firms. Any unsolicited resumes submitted without a signed agreement will be considered the property of Gimlet Labs, and no fees will be paid.
","descriptionPlain":"About us\n\nGimlet is building the first multi-silicon neocloud designed for fast, efficient AI inference.\n\nWe combine large-scale compute infrastructure with an execution platform that partitions AI workloads and maps each stage to the hardware best suited to run it.\n\nWe work with foundation labs, hyperscalers, and AI-native companies, giving our team access to technical problems spanning frontier models, production infrastructure, and emerging hardware.\n\nAbout the role\n\nGimlet Labs is seeking a Data Center Facilities Operations Lead to own the critical facilities operating model for Gimlet data centers and high-density AI infrastructure deployments. In this role, you will make sure the facility-side systems that support Gimlet's compute capacity are ready, monitored, maintained, and operating inside the required envelope.\n\nYou will focus on the infrastructure that keeps liquid-cooled AI systems healthy: facility water loops, CDUs, supply and return temperatures, flow, pressure, water quality, leak detection, alarms, heat rejection, power and cooling coordination, BMS/DCIM telemetry, maintenance procedures, and vendor repair workflows.\n\nThis role is well-suited for a critical facilities operator who understands data center MEP systems, liquid cooling, operational monitoring, and the discipline required to keep high-density compute environments stable as Gimlet scales.\n\nWhat success looks like\n\nIn the first 12-18 months, you will:\n\n - Build the facilities operations model for current and future Gimlet sites, including operating standards, escalation paths, maintenance routines, acceptance criteria, and facility readiness gates.\n\n - Translate OEM and engineering requirements for liquid-cooled platforms into practical site operating envelopes for temperature, flow, pressure, water quality, alarms, and heat rejection.\n\n - Own monitoring and response for facility-side telemetry, including supply and return water temperatures, delta-T, flow, pressure, leak detection, CDU status, cooling capacity margins, and BMS/DCIM alarms.\n\n - Partner with colocation providers, facility vendors, OEMs, Site Managers, Data Center Technicians, Deployment Leads, and TPMs to ensure facilities are ready before new compute capacity is deployed.\n\n - Create and maintain MOPs, SOPs, EOPs, maintenance windows, runbooks, inspection routines, and incident response procedures for critical facilities and liquid cooling operations.\n\n - Coordinate preventive maintenance, repairs, and vendor response for CDUs, facility water loops, filters, valves, pumps, sensors, leak detection systems, chillers, dry coolers, CRAHs, and related infrastructure.\n\n - Lead facility-side root cause analysis for thermal, leak, power, cooling, monitoring, and environmental events, then drive durable corrective actions.\n\n - Build reporting that shows facility health, risk, readiness, capacity margin, recurring issues, open repairs, and operational trends across Gimlet sites.\n\nYou may be a good fit if you have\n\n - Experience in data center facilities operations, critical facilities engineering, MEP operations, commissioning, or facilities maintenance\n\n - Experience operating liquid-cooled, high-density compute infrastructure\n\n - Familiarity with facility water systems, CDUs, heat rejection, leak detection, and water-quality controls\n\n - Experience using BMS, DCIM, EPMS, or similar systems to monitor and respond to facility conditions\n\n - The ability to create and execute operational procedures with strong attention to safety and reliability\n\n - Experience coordinating across site teams, colocation providers, OEMs, and facilities vendors\n\n - The ability to work in active data center environments and support urgent facilities escalations\n\nStrong candidates may also have\n\n - Experience supporting GPU, HPC, or rack-scale liquid-cooled infrastructure\n\n - Experience with commissioning, integrated systems testing, site acceptance, or facility turnover\n\n - Familiarity with power distribution, UPS and generator systems, chilled water, dry coolers, CRAH/CRAC systems, or rear-door heat exchangers\n\n - Experience managing colocation obligations, service levels, maintenance windows, and vendor escalations\n\n - A track record of improving facility reliability through monitoring, preventive maintenance, and incident analysis\n\nWhy join now?\n\nGimlet is expanding from its core technology into a production neocloud spanning new hardware, customers, and data centers.\n\n - Solve hard problems.\n\n - Own meaningful work.\n\n - Build for production.\n\n - Help define what’s next.\n\nAgency Policy: Gimlet Labs does not accept unsolicited resumes from recruitment agencies or search firms. Any unsolicited resumes submitted without a signed agreement will be considered the property of Gimlet Labs, and no fees will be paid."},{"id":"03c2bde9-b2cf-4dc7-976a-c965eec45f9e","title":"Executive Business Partner","department":"G&A","team":"G&A","employmentType":"FullTime","location":"San Francisco, CA","secondaryLocations":[],"publishedAt":"2026-09-03T15:09:26.641+00:00","isListed":true,"isRemote":false,"workplaceType":"OnSite","address":{"postalAddress":{"postalCode":"94103","addressRegion":"California","streetAddress":"255 Portrero Ave","addressCountry":"United States","addressLocality":"San Francisco"}},"jobUrl":"https://jobs.ashbyhq.com/gimlet/03c2bde9-b2cf-4dc7-976a-c965eec45f9e","applyUrl":"https://jobs.ashbyhq.com/gimlet/03c2bde9-b2cf-4dc7-976a-c965eec45f9e/application","descriptionHtml":"About us
Gimlet is building the first multi-silicon neocloud designed for fast, efficient AI inference.
We combine large-scale compute infrastructure with an execution platform that partitions AI workloads and maps each stage to the hardware best suited to run it.
We work with foundation labs, hyperscalers, and AI-native companies, giving our team access to technical problems spanning frontier models, production infrastructure, and emerging hardware.
About the role
As an Executive Business Partner, you will help Gimlet’s executives and leadership team operate effectively as the company scales. You will learn how each executive works, connect priorities across the team, and clear the runway so leaders can remain focused on the company’s most important work.
You will manage complex schedules, communications, travel, and follow-through while serving as a liaison among the executives you support. This role requires someone who can anticipate needs, bring structure to ambiguity, and represent Gimlet with polish and professionalism in communications with investors and other external partners.
This role is based in our San Francisco office and requires a consistent on-site presence.
What success looks like
In the first 12-18 months, you will:
Develop a strong understanding of each executive’s priorities, responsibilities, and working style
Serve as a trusted liaison across the executives you support, ensuring information and decisions move efficiently
Manage complex calendars, meetings, and travel in a way that reflects company priorities
Anticipate needs, resolve conflicts, and remove operational friction before it slows the team down
Triage communications, draft polished responses, and ensure important matters receive timely follow-up
Track commitments, decisions, and action items across the leadership team
Improve administrative workflows and create systems that make the executive team more effective
Coordinate leadership meetings, company events, offsites, and other important moments
You may be a good fit if you have
Experience supporting multiple executives or senior leaders in a startup, venture firm, or other fast-paced environment
Exceptional attention to detail and organizational skills
Strong written and verbal communication skills, including the ability to communicate professionally with investors and external partners
Sound judgment and discretion when handling confidential or sensitive matters
The ability to prioritize competing demands and adjust quickly as circumstances change
A proactive approach to identifying needs, solving problems, and following through
Comfort bringing structure to ambiguous or loosely defined situations
The ability to work on-site in Gimlet’s San Francisco office
Strong candidates may also have
Experience supporting founders or a fast-growing executive team
Experience coordinating complex domestic and international travel
Experience working with investors, board members, customers, or other senior external stakeholders
A track record of building administrative systems and improving executive workflows
Experience planning leadership meetings, company events, or team offsites
Why join now?
Gimlet is expanding from its core technology into a production neocloud spanning new hardware, customers, and data centers.
Solve hard problems.
Own meaningful work.
Build for production.
Help define what’s next.
Agency Policy: Gimlet Labs does not accept unsolicited resumes from recruitment agencies or search firms. Any unsolicited resumes submitted without a signed agreement will be considered the property of Gimlet Labs, and no fees will be paid.
","descriptionPlain":"About us\n\nGimlet is building the first multi-silicon neocloud designed for fast, efficient AI inference.\n\nWe combine large-scale compute infrastructure with an execution platform that partitions AI workloads and maps each stage to the hardware best suited to run it.\n\nWe work with foundation labs, hyperscalers, and AI-native companies, giving our team access to technical problems spanning frontier models, production infrastructure, and emerging hardware.\n\nAbout the role\n\nAs an Executive Business Partner, you will help Gimlet’s executives and leadership team operate effectively as the company scales. You will learn how each executive works, connect priorities across the team, and clear the runway so leaders can remain focused on the company’s most important work.\n\nYou will manage complex schedules, communications, travel, and follow-through while serving as a liaison among the executives you support. This role requires someone who can anticipate needs, bring structure to ambiguity, and represent Gimlet with polish and professionalism in communications with investors and other external partners.\n\nThis role is based in our San Francisco office and requires a consistent on-site presence.\n\nWhat success looks like\n\nIn the first 12-18 months, you will:\n\n - Develop a strong understanding of each executive’s priorities, responsibilities, and working style\n\n - Serve as a trusted liaison across the executives you support, ensuring information and decisions move efficiently\n\n - Manage complex calendars, meetings, and travel in a way that reflects company priorities\n\n - Anticipate needs, resolve conflicts, and remove operational friction before it slows the team down\n\n - Triage communications, draft polished responses, and ensure important matters receive timely follow-up\n\n - Track commitments, decisions, and action items across the leadership team\n\n - Improve administrative workflows and create systems that make the executive team more effective\n\n - Coordinate leadership meetings, company events, offsites, and other important moments\n\nYou may be a good fit if you have\n\n - Experience supporting multiple executives or senior leaders in a startup, venture firm, or other fast-paced environment\n\n - Exceptional attention to detail and organizational skills\n\n - Strong written and verbal communication skills, including the ability to communicate professionally with investors and external partners\n\n - Sound judgment and discretion when handling confidential or sensitive matters\n\n - The ability to prioritize competing demands and adjust quickly as circumstances change\n\n - A proactive approach to identifying needs, solving problems, and following through\n\n - Comfort bringing structure to ambiguous or loosely defined situations\n\n - The ability to work on-site in Gimlet’s San Francisco office\n\nStrong candidates may also have\n\n - Experience supporting founders or a fast-growing executive team\n\n - Experience coordinating complex domestic and international travel\n\n - Experience working with investors, board members, customers, or other senior external stakeholders\n\n - A track record of building administrative systems and improving executive workflows\n\n - Experience planning leadership meetings, company events, or team offsites\n\nWhy join now?\n\nGimlet is expanding from its core technology into a production neocloud spanning new hardware, customers, and data centers.\n\n - Solve hard problems.\n\n - Own meaningful work.\n\n - Build for production.\n\n - Help define what’s next.\n\nAgency Policy: Gimlet Labs does not accept unsolicited resumes from recruitment agencies or search firms. Any unsolicited resumes submitted without a signed agreement will be considered the property of Gimlet Labs, and no fees will be paid."},{"id":"fb3653a5-5897-451a-a7c5-5ae733387c97","title":"Corporate Controller","department":"G&A","team":"Finance","employmentType":"FullTime","location":"San Francisco, CA","secondaryLocations":[],"publishedAt":"2026-09-30T21:46:49.084+00:00","isListed":true,"isRemote":false,"workplaceType":"OnSite","address":{"postalAddress":{"postalCode":"94103","addressRegion":"California","streetAddress":"255 Portrero Ave","addressCountry":"United States","addressLocality":"San Francisco"}},"jobUrl":"https://jobs.ashbyhq.com/gimlet/fb3653a5-5897-451a-a7c5-5ae733387c97","applyUrl":"https://jobs.ashbyhq.com/gimlet/fb3653a5-5897-451a-a7c5-5ae733387c97/application","descriptionHtml":"About us
Gimlet is building the first multi-silicon neocloud designed for fast, efficient AI inference.
We combine large-scale compute infrastructure with an execution platform that partitions AI workloads and maps each stage to the hardware best suited to run it.
We work with foundation labs, hyperscalers, and AI-native companies, giving our team access to technical problems spanning frontier models, production infrastructure, and emerging hardware.
About the role
As Gimlet's Corporate Controller, you will own the accounting and financial operations foundation that supports the company as we scale.
This is a build role, not a maintain role. Gimlet is growing quickly across customers, compute infrastructure, data centers, and financial complexity. We need someone who has operated through this stage before and can build the systems, processes, controls, and team required for what comes next.
You will own Accounting and Financial Operations end to end, spanning close and reporting, technical accounting, audit, payroll, equity administration, cash management, procurement, AP, billing, and collections. You will also lead the evolution of our finance systems and controls as we build toward public-company readiness.
This is a highly cross-functional role, partnering with Finance, GTM, Commerce, Engineering, Infrastructure, Legal, and company leadership. We're looking for someone with deep accounting expertise who is equally comfortable getting into the operational details of how the business works.
What success looks like
In your first 12–18 months, you will:
Own Gimlet's monthly, quarterly, and annual close across a growing multi-entity organization, improving the speed and reliability of financial reporting.
Lead consolidations and accounting across corporate entities and SPVs, partnering with Finance, Tax, Treasury, and Legal on complex transactions.
Own technical accounting across revenue recognition, leases, fixed assets, stock-based compensation, and complex commercial arrangements.
Lead external audits and build the policies, controls, and operating discipline required as Gimlet moves toward public-company readiness.
Own Financial Operations across payroll, equity administration, cash operations, procurement, AP, billing, collections, and AR.
Build scalable billing and collections processes as Gimlet introduces increasingly complex commercial and usage-based pricing models.
Partner with Commerce and Engineering to translate how Gimlet's products are priced and consumed into reliable billing, accounting, and financial operations.
Own and evolve Gimlet's accounting technology stack, including ERP implementation and adjacent financial systems.
Use automation and AI to eliminate repetitive work and improve the team's efficiency and accuracy.
Proactively evaluate material contracts and new business arrangements for accounting and operational implications.
Build and develop the Accounting and Financial Operations organization as Gimlet scales.
You may be a good fit if you have
10+ years of progressive accounting experience with meaningful ownership of Accounting and Financial Operations.
Operated as a Controller, Assistant Controller with significant ownership, or equivalent accounting leader inside a rapidly scaling company.
Experience building an accounting function through meaningful private-company growth rather than only operating mature processes.
Deep, current US GAAP expertise across revenue recognition, fixed assets, leases, stock-based compensation, and complex commercial arrangements.
Personally owned a full close and consolidation across multiple legal entities.
Hands-on experience across payroll, equity administration, AP, billing, collections, and cash operations.
Led external audits and helped build the systems, controls, and policies required for increasing audit and public-company readiness.
Experience implementing or scaling an ERP and surrounding financial systems.
Strong cross-functional judgment and the ability to communicate complex accounting issues clearly to non-accountants.
The ability to operate independently at high volume and build the processes you wish existed.
Strong candidates may also have
Started their career in Big Four public accounting before moving into industry.
Experience preparing a high-growth private company for an IPO or operating through the transition to becoming public.
Experience in AI infrastructure, cloud, data centers, hardware, SaaS, or another financially complex technology business.
Experience with usage-based pricing, high-volume billing, SPVs, equipment financing, or infrastructure-related arrangements.
Experience partnering closely with FP&A, Strategic Finance, or Capital Markets.
Experience building and leading accounting and financial operations teams through rapid growth.
CPA or equivalent accounting qualification.
Why join now?
Gimlet is expanding from its core technology into a production neocloud spanning new hardware, customers, and data centers.
Solve hard problems.
Own meaningful work.
Build for production.
Help define what’s next.
Agency Policy: Gimlet Labs does not accept unsolicited resumes from recruitment agencies or search firms. Any unsolicited resumes submitted without a signed agreement will be considered the property of Gimlet Labs, and no fees will be paid.
","descriptionPlain":"About us\n\nGimlet is building the first multi-silicon neocloud designed for fast, efficient AI inference.\n\nWe combine large-scale compute infrastructure with an execution platform that partitions AI workloads and maps each stage to the hardware best suited to run it.\n\nWe work with foundation labs, hyperscalers, and AI-native companies, giving our team access to technical problems spanning frontier models, production infrastructure, and emerging hardware.\n\nAbout the role\n\nAs Gimlet's Corporate Controller, you will own the accounting and financial operations foundation that supports the company as we scale.\n\nThis is a build role, not a maintain role. Gimlet is growing quickly across customers, compute infrastructure, data centers, and financial complexity. We need someone who has operated through this stage before and can build the systems, processes, controls, and team required for what comes next.\n\nYou will own Accounting and Financial Operations end to end, spanning close and reporting, technical accounting, audit, payroll, equity administration, cash management, procurement, AP, billing, and collections. You will also lead the evolution of our finance systems and controls as we build toward public-company readiness.\n\nThis is a highly cross-functional role, partnering with Finance, GTM, Commerce, Engineering, Infrastructure, Legal, and company leadership. We're looking for someone with deep accounting expertise who is equally comfortable getting into the operational details of how the business works.\n\nWhat success looks like\n\nIn your first 12–18 months, you will:\n\n - Own Gimlet's monthly, quarterly, and annual close across a growing multi-entity organization, improving the speed and reliability of financial reporting.\n\n - Lead consolidations and accounting across corporate entities and SPVs, partnering with Finance, Tax, Treasury, and Legal on complex transactions.\n\n - Own technical accounting across revenue recognition, leases, fixed assets, stock-based compensation, and complex commercial arrangements.\n\n - Lead external audits and build the policies, controls, and operating discipline required as Gimlet moves toward public-company readiness.\n\n - Own Financial Operations across payroll, equity administration, cash operations, procurement, AP, billing, collections, and AR.\n\n - Build scalable billing and collections processes as Gimlet introduces increasingly complex commercial and usage-based pricing models.\n\n - Partner with Commerce and Engineering to translate how Gimlet's products are priced and consumed into reliable billing, accounting, and financial operations.\n\n - Own and evolve Gimlet's accounting technology stack, including ERP implementation and adjacent financial systems.\n\n - Use automation and AI to eliminate repetitive work and improve the team's efficiency and accuracy.\n\n - Proactively evaluate material contracts and new business arrangements for accounting and operational implications.\n\n - Build and develop the Accounting and Financial Operations organization as Gimlet scales.\n\nYou may be a good fit if you have\n\n - 10+ years of progressive accounting experience with meaningful ownership of Accounting and Financial Operations.\n\n - Operated as a Controller, Assistant Controller with significant ownership, or equivalent accounting leader inside a rapidly scaling company.\n\n - Experience building an accounting function through meaningful private-company growth rather than only operating mature processes.\n\n - Deep, current US GAAP expertise across revenue recognition, fixed assets, leases, stock-based compensation, and complex commercial arrangements.\n\n - Personally owned a full close and consolidation across multiple legal entities.\n\n - Hands-on experience across payroll, equity administration, AP, billing, collections, and cash operations.\n\n - Led external audits and helped build the systems, controls, and policies required for increasing audit and public-company readiness.\n\n - Experience implementing or scaling an ERP and surrounding financial systems.\n\n - Strong cross-functional judgment and the ability to communicate complex accounting issues clearly to non-accountants.\n\n - The ability to operate independently at high volume and build the processes you wish existed.\n\nStrong candidates may also have\n\n - Started their career in Big Four public accounting before moving into industry.\n\n - Experience preparing a high-growth private company for an IPO or operating through the transition to becoming public.\n\n - Experience in AI infrastructure, cloud, data centers, hardware, SaaS, or another financially complex technology business.\n\n - Experience with usage-based pricing, high-volume billing, SPVs, equipment financing, or infrastructure-related arrangements.\n\n - Experience partnering closely with FP&A, Strategic Finance, or Capital Markets.\n\n - Experience building and leading accounting and financial operations teams through rapid growth.\n\n - CPA or equivalent accounting qualification.\n\nWhy join now?\n\nGimlet is expanding from its core technology into a production neocloud spanning new hardware, customers, and data centers.\n\n - Solve hard problems.\n\n - Own meaningful work.\n\n - Build for production.\n\n - Help define what’s next.\n\nAgency Policy: Gimlet Labs does not accept unsolicited resumes from recruitment agencies or search firms. Any unsolicited resumes submitted without a signed agreement will be considered the property of Gimlet Labs, and no fees will be paid."},{"id":"74cb8e21-987a-40d5-977d-b8ce7f46f753","title":"Director of FP&A","department":"G&A","team":"Finance","employmentType":"FullTime","location":"San Francisco, CA","secondaryLocations":[],"publishedAt":"2026-10-01T23:41:02.991+00:00","isListed":true,"isRemote":false,"workplaceType":"OnSite","address":{"postalAddress":{"postalCode":"94103","addressRegion":"California","streetAddress":"255 Portrero Ave","addressCountry":"United States","addressLocality":"San Francisco"}},"jobUrl":"https://jobs.ashbyhq.com/gimlet/74cb8e21-987a-40d5-977d-b8ce7f46f753","applyUrl":"https://jobs.ashbyhq.com/gimlet/74cb8e21-987a-40d5-977d-b8ce7f46f753/application","descriptionHtml":"About us
Gimlet is building the first multi-silicon neocloud designed for fast, efficient AI inference.
We combine large-scale compute infrastructure with an execution platform that partitions AI workloads and maps each stage to the hardware best suited to run it.
We work with foundation labs, hyperscalers, and AI-native companies, giving our team access to technical problems spanning frontier models, production infrastructure, and emerging hardware.
As Director of FP&A, you will build Gimlet’s FP&A function and help shape the financial decisions behind our growth. You will own the consolidated financial model, budgeting, forecasting, and performance analysis for a rapidly scaling AI infrastructure business. Cash burn, liquidity, runway, and the economics of deploying compute will be central to your work.
You will partner with executive leadership, Strategic Finance, Infrastructure, and GTM to translate company strategy into operating plans and capital allocation decisions. You will build models that connect customer demand, infrastructure investment, and operating costs, giving leadership clear visibility into the assumptions, risks, and tradeoffs behind each plan.
This is a hands-on leadership role for someone who combines strong financial modeling with business judgment and data fluency. You will establish the tools, processes, and team needed to support Gimlet’s next stage of growth.
In the first 12–18 months, you will:
Lead the end-to-end annual operating plan and budget process, including periodic reforecasts. Track performance against plan, identify risks and opportunities, and partner with department heads to optimize costs and translate financial insights into operational decisions.
Own the consolidated financial model and operating assumptions, with clear visibility into business unit economics. Integrate inputs from Strategic Finance and other teams to develop accurate, data-driven projections and guidance.
Establish the financial framework for allocating resources across headcount, spending, and capacity.
Own the consolidated cash forecast and analysis of runway and net working capital, including operating cash flow scenarios and sensitivities.
Conduct forward-looking scenario and sensitivity analyses and historical variance analyses, translating findings into clear, actionable recommendations.
Partner with key stakeholders on capacity planning, customer contracts, leases, and other strategic projects. Build dynamic, well-structured models with stress-tested assumptions, clear outputs, and documented logic.
Partner with GTM on commercial FP&A, providing projections and analysis to inform revenue planning and commercial decisions.
Partner with Strategic Finance to build and maintain long-range planning, incorporating growth scenarios, financial targets, and the impact of strategic initiatives.
Partner with Accounting to establish timely, high-quality monthly and quarterly financial reporting.
Develop and refine KPIs and regularly updated dashboards that measure financial and operational performance. Report to executive leadership and prepare analysis and narratives for board and investor materials.
Build and lead a high-performing FP&A team, continuously improving tools, models, and processes as the company scales.
Approximately 8–10+ years of experience in FP&A, strategic finance, or investment banking combined with operating finance.
Demonstrated ownership of consolidated models, budgets, forecasts, and budget-versus-actual analysis.
Strong modeling skills, including independently developing assumptions, evaluating scenarios, and identifying key business drivers.
Substantial exposure to data centers or large-scale infrastructure capex, including planning and monitoring major investments.
An understanding of how hardware capex, deployment costs, power, cooling, and operating expenses affect margins, depreciation, and cash flow.
Strong data analytics skills and the ability to translate complex information into clear recommendations.
Experience partnering with technical teams and business leaders and communicating to financial and nonfinancial audiences.
Experience at a hyperscaler, neocloud, colocation provider, data center developer, or another infrastructure-intensive operator.
A background in digital infrastructure banking, investing, or project finance, paired with in-house planning experience.
Familiarity with AI infrastructure revenue models, usage-based forecasting, and inference unit economics.
Experience building an FP&A function, implementing planning software, and developing a team.
Experience scaling a technology company through rapid growth and significant capital investment.
Familiarity with debt covenants and incorporating financing obligations into forecasts.
Experience preparing board and investor materials and evaluating customer contracts, leases, or strategic transactions.
Why join now?
Gimlet is expanding from its core technology into a production neocloud spanning new hardware, customers, and data centers.
Solve hard problems.
Own meaningful work.
Build for production.
Help define what’s next.
Agency Policy: Gimlet Labs does not accept unsolicited resumes from recruitment agencies or search firms. Any unsolicited resumes submitted without a signed agreement will be considered the property of Gimlet Labs, and no fees will be paid.
","descriptionPlain":"About us\n\nGimlet is building the first multi-silicon neocloud designed for fast, efficient AI inference.\n\nWe combine large-scale compute infrastructure with an execution platform that partitions AI workloads and maps each stage to the hardware best suited to run it.\n\nWe work with foundation labs, hyperscalers, and AI-native companies, giving our team access to technical problems spanning frontier models, production infrastructure, and emerging hardware.\n\n\nABOUT THE ROLE\n\nAs Director of FP&A, you will build Gimlet’s FP&A function and help shape the financial decisions behind our growth. You will own the consolidated financial model, budgeting, forecasting, and performance analysis for a rapidly scaling AI infrastructure business. Cash burn, liquidity, runway, and the economics of deploying compute will be central to your work.\n\nYou will partner with executive leadership, Strategic Finance, Infrastructure, and GTM to translate company strategy into operating plans and capital allocation decisions. You will build models that connect customer demand, infrastructure investment, and operating costs, giving leadership clear visibility into the assumptions, risks, and tradeoffs behind each plan.\n\nThis is a hands-on leadership role for someone who combines strong financial modeling with business judgment and data fluency. You will establish the tools, processes, and team needed to support Gimlet’s next stage of growth.\n\n\n\n\nWHAT SUCCESS LOOKS LIKE\n\nIn the first 12–18 months, you will:\n\n - Lead the end-to-end annual operating plan and budget process, including periodic reforecasts. Track performance against plan, identify risks and opportunities, and partner with department heads to optimize costs and translate financial insights into operational decisions.\n\n - Own the consolidated financial model and operating assumptions, with clear visibility into business unit economics. Integrate inputs from Strategic Finance and other teams to develop accurate, data-driven projections and guidance.\n\n - Establish the financial framework for allocating resources across headcount, spending, and capacity.\n\n - Own the consolidated cash forecast and analysis of runway and net working capital, including operating cash flow scenarios and sensitivities.\n\n - Conduct forward-looking scenario and sensitivity analyses and historical variance analyses, translating findings into clear, actionable recommendations.\n\n - Partner with key stakeholders on capacity planning, customer contracts, leases, and other strategic projects. Build dynamic, well-structured models with stress-tested assumptions, clear outputs, and documented logic.\n\n - Partner with GTM on commercial FP&A, providing projections and analysis to inform revenue planning and commercial decisions.\n\n - Partner with Strategic Finance to build and maintain long-range planning, incorporating growth scenarios, financial targets, and the impact of strategic initiatives.\n\n - Partner with Accounting to establish timely, high-quality monthly and quarterly financial reporting.\n\n - Develop and refine KPIs and regularly updated dashboards that measure financial and operational performance. Report to executive leadership and prepare analysis and narratives for board and investor materials.\n\n - Build and lead a high-performing FP&A team, continuously improving tools, models, and processes as the company scales.\n \n \n\n\nYOU MAY BE A GOOD FIT IF YOU HAVE\n\n - Approximately 8–10+ years of experience in FP&A, strategic finance, or investment banking combined with operating finance.\n\n - Demonstrated ownership of consolidated models, budgets, forecasts, and budget-versus-actual analysis.\n\n - Strong modeling skills, including independently developing assumptions, evaluating scenarios, and identifying key business drivers.\n\n - Substantial exposure to data centers or large-scale infrastructure capex, including planning and monitoring major investments.\n\n - An understanding of how hardware capex, deployment costs, power, cooling, and operating expenses affect margins, depreciation, and cash flow.\n\n - Strong data analytics skills and the ability to translate complex information into clear recommendations.\n\n - Experience partnering with technical teams and business leaders and communicating to financial and nonfinancial audiences.\n \n \n\n\nSTRONG CANDIDATES MAY ALSO HAVE\n\n - Experience at a hyperscaler, neocloud, colocation provider, data center developer, or another infrastructure-intensive operator.\n\n - A background in digital infrastructure banking, investing, or project finance, paired with in-house planning experience.\n\n - Familiarity with AI infrastructure revenue models, usage-based forecasting, and inference unit economics.\n\n - Experience building an FP&A function, implementing planning software, and developing a team.\n\n - Experience scaling a technology company through rapid growth and significant capital investment.\n\n - Familiarity with debt covenants and incorporating financing obligations into forecasts.\n\n - Experience preparing board and investor materials and evaluating customer contracts, leases, or strategic transactions.\n\nWhy join now?\n\nGimlet is expanding from its core technology into a production neocloud spanning new hardware, customers, and data centers.\n\n - Solve hard problems.\n\n - Own meaningful work.\n\n - Build for production.\n\n - Help define what’s next.\n\nAgency Policy: Gimlet Labs does not accept unsolicited resumes from recruitment agencies or search firms. Any unsolicited resumes submitted without a signed agreement will be considered the property of Gimlet Labs, and no fees will be paid."},{"id":"965c51f6-41f2-4b44-9965-52ad42187af4","title":"Senior Technical Recruiter","department":"G&A","team":"G&A","employmentType":"FullTime","location":"San Francisco, CA","secondaryLocations":[],"publishedAt":"2026-05-05T18:33:35.862+00:00","isListed":true,"isRemote":false,"workplaceType":"OnSite","address":{"postalAddress":{"postalCode":"94103","addressRegion":"California","streetAddress":"255 Portrero Ave","addressCountry":"United States","addressLocality":"San Francisco"}},"jobUrl":"https://jobs.ashbyhq.com/gimlet/965c51f6-41f2-4b44-9965-52ad42187af4","applyUrl":"https://jobs.ashbyhq.com/gimlet/965c51f6-41f2-4b44-9965-52ad42187af4/application","descriptionHtml":"About us
Gimlet is building the first multi-silicon neocloud designed for fast, efficient AI inference.
We combine large-scale compute infrastructure with an execution platform that partitions AI workloads and maps each stage to the hardware best suited to run it.
We work with foundation labs, hyperscalers, and AI-native companies, giving our team access to technical problems spanning frontier models, production infrastructure, and emerging hardware.
Gimlet Labs is hiring a Senior Technical Recruiter to help build one of the most technically ambitious engineering teams in AI infrastructure.
We are building systems that make heterogeneous compute work together at scale, from compiler and kernel optimization to distributed systems and production inference infrastructure. Recruiting for these teams means finding exceptional engineers who operate at the intersection of hardware, software, and performance.
You will partner directly with engineering leadership to understand complex technical challenges, develop creative sourcing strategies, and engage talent who are rarely actively looking for their next opportunity.
We are looking for someone who loves the craft of technical recruiting: understanding what makes an engineer exceptional, finding talent others overlook, building genuine relationships, and helping great people make consequential career decisions.
In your first 12–18 months, you will:
Build exceptional engineering teams. Identify, engage, and close high-caliber engineers across compiler, kernel, ML infrastructure, distributed systems, and other specialized technical disciplines.
Become a trusted partner to engineering leadership. Develop a deep understanding of the technical challenges we're solving and help hiring managers make informed decisions about talent, hiring priorities, and market opportunities.
Build differentiated talent pipelines. Develop creative sourcing strategies, map relevant companies and engineering teams, and build relationships with exceptional engineers who are difficult to reach through traditional recruiting channels.
Own searches from start to finish. Drive recruiting processes with urgency, strong candidate engagement, and clear accountability, from initial sourcing through offer acceptance.
Raise the quality of our hiring process. Partner with interviewers to improve evaluation consistency, candidate experience, and decision-making while maintaining a high technical bar.
Help scale the Talent Acquisition function. Identify opportunities to improve our tools, workflows, and sourcing practices, building processes that make the team more effective as Gimlet grows.
5+ years of technical recruiting experience, with a track record of hiring exceptional engineers in competitive talent markets.
Experience recruiting across specialized engineering disciplines such as compilers, kernels, ML infrastructure, distributed systems, networking, or HPC.
Strong technical curiosity and the ability to understand complex engineering domains beyond matching keywords.
Exceptional sourcing skills, with demonstrated success identifying, engaging, and closing passive talent.
Strong judgment in evaluating engineering talent and partnering with technical leaders on hiring decisions.
High ownership and the ability to independently drive searches from sourcing strategy through offer acceptance in a fast-moving environment.
Direct experience recruiting compiler, GPU kernel, or low-level performance engineers.
Experience hiring for AI infrastructure, high-performance computing, or distributed systems teams.
A strong network within specialized engineering communities and a track record of engaging hard-to-reach talent.
Experience building recruiting strategies and processes at early-stage or high-growth technology companies
Why join now?
Gimlet is expanding from its core technology into a production neocloud spanning new hardware, customers, and data centers.
Solve hard problems.
Own meaningful work.
Build for production.
Help define what’s next.
Agency Policy: Gimlet Labs does not accept unsolicited resumes from recruitment agencies or search firms. Any unsolicited resumes submitted without a signed agreement will be considered the property of Gimlet Labs, and no fees will be paid.
","descriptionPlain":"About us\n\nGimlet is building the first multi-silicon neocloud designed for fast, efficient AI inference.\n\nWe combine large-scale compute infrastructure with an execution platform that partitions AI workloads and maps each stage to the hardware best suited to run it.\n\nWe work with foundation labs, hyperscalers, and AI-native companies, giving our team access to technical problems spanning frontier models, production infrastructure, and emerging hardware.\n\n\nABOUT THE ROLE \n\nGimlet Labs is hiring a Senior Technical Recruiter to help build one of the most technically ambitious engineering teams in AI infrastructure.\n\nWe are building systems that make heterogeneous compute work together at scale, from compiler and kernel optimization to distributed systems and production inference infrastructure. Recruiting for these teams means finding exceptional engineers who operate at the intersection of hardware, software, and performance.\n\nYou will partner directly with engineering leadership to understand complex technical challenges, develop creative sourcing strategies, and engage talent who are rarely actively looking for their next opportunity.\n\nWe are looking for someone who loves the craft of technical recruiting: understanding what makes an engineer exceptional, finding talent others overlook, building genuine relationships, and helping great people make consequential career decisions.\n\n\nWHAT SUCCESS LOOKS LIKE\n\nIn your first 12–18 months, you will:\n\n - Build exceptional engineering teams. Identify, engage, and close high-caliber engineers across compiler, kernel, ML infrastructure, distributed systems, and other specialized technical disciplines.\n\n - Become a trusted partner to engineering leadership. Develop a deep understanding of the technical challenges we're solving and help hiring managers make informed decisions about talent, hiring priorities, and market opportunities.\n\n - Build differentiated talent pipelines. Develop creative sourcing strategies, map relevant companies and engineering teams, and build relationships with exceptional engineers who are difficult to reach through traditional recruiting channels.\n\n - Own searches from start to finish. Drive recruiting processes with urgency, strong candidate engagement, and clear accountability, from initial sourcing through offer acceptance.\n\n - Raise the quality of our hiring process. Partner with interviewers to improve evaluation consistency, candidate experience, and decision-making while maintaining a high technical bar.\n\n - Help scale the Talent Acquisition function. Identify opportunities to improve our tools, workflows, and sourcing practices, building processes that make the team more effective as Gimlet grows.\n\n\nYOU MAY BE A GOOD FIT IF YOU HAVE\n\n - 5+ years of technical recruiting experience, with a track record of hiring exceptional engineers in competitive talent markets.\n\n - Experience recruiting across specialized engineering disciplines such as compilers, kernels, ML infrastructure, distributed systems, networking, or HPC.\n\n - Strong technical curiosity and the ability to understand complex engineering domains beyond matching keywords.\n\n - Exceptional sourcing skills, with demonstrated success identifying, engaging, and closing passive talent.\n\n - Strong judgment in evaluating engineering talent and partnering with technical leaders on hiring decisions.\n\n - High ownership and the ability to independently drive searches from sourcing strategy through offer acceptance in a fast-moving environment.\n\n\nSTRONG CANDIDATES MAY ALSO HAVE\n\n - Direct experience recruiting compiler, GPU kernel, or low-level performance engineers.\n\n - Experience hiring for AI infrastructure, high-performance computing, or distributed systems teams.\n\n - A strong network within specialized engineering communities and a track record of engaging hard-to-reach talent.\n\n - Experience building recruiting strategies and processes at early-stage or high-growth technology companies\n\nWhy join now?\n\nGimlet is expanding from its core technology into a production neocloud spanning new hardware, customers, and data centers.\n\n - Solve hard problems.\n\n - Own meaningful work.\n\n - Build for production.\n\n - Help define what’s next.\n\nAgency Policy: Gimlet Labs does not accept unsolicited resumes from recruitment agencies or search firms. Any unsolicited resumes submitted without a signed agreement will be considered the property of Gimlet Labs, and no fees will be paid."},{"id":"17555b19-7435-4466-8792-6d6d0d2430d5","title":"Senior Technical Recruiter, Data Center Infrastructure","department":"G&A","team":"G&A","employmentType":"FullTime","location":"San Francisco, CA","secondaryLocations":[],"publishedAt":"2026-10-13T16:50:14.556+00:00","isListed":true,"isRemote":false,"workplaceType":"OnSite","address":{"postalAddress":{"postalCode":"94103","addressRegion":"California","streetAddress":"255 Portrero Ave","addressCountry":"United States","addressLocality":"San Francisco"}},"jobUrl":"https://jobs.ashbyhq.com/gimlet/17555b19-7435-4466-8792-6d6d0d2430d5","applyUrl":"https://jobs.ashbyhq.com/gimlet/17555b19-7435-4466-8792-6d6d0d2430d5/application","descriptionHtml":"About us
Gimlet is building the first multi-silicon neocloud designed for fast, efficient AI inference.
We combine large-scale compute infrastructure with an execution platform that partitions AI workloads and maps each stage to the hardware best suited to run it.
We work with foundation labs, hyperscalers, and AI-native companies, giving our team access to technical problems spanning frontier models, production infrastructure, and emerging hardware.
About the role
Gimlet Labs is hiring a Senior Technical Recruiter to help build the teams responsible for expanding and operating our next generation of AI data centers.
As demand for AI compute accelerates, securing power, delivering capacity, and operating reliable infrastructure at scale are increasingly critical challenges. We're building the teams that turn ambitious infrastructure plans into operational data centers, from site sourcing and capacity development to engineering, deployment, and ongoing operations.
You will partner directly with infrastructure and operations leadership to identify exceptional talent across highly specialized disciplines, develop creative sourcing strategies, and build relationships with the people who know how to deliver complex infrastructure at scale.
We are looking for someone who loves the craft of recruiting, understands the value of deep industry relationships, and is excited to help build the physical infrastructure powering the next generation of AI.
What success looks like
In your first 6–12 months, you will:
Build exceptional infrastructure teams. Identify, engage, and close talent across data center operations, critical facilities, site sourcing, capacity delivery, infrastructure engineering, and technical operations.
Become a trusted partner to infrastructure leadership. Understand our expansion priorities, technical requirements, and operational challenges to help hiring managers make informed decisions about talent and hiring strategy.
Build differentiated talent pipelines. Map relevant companies, data center operators, hyperscalers, and infrastructure teams to identify and engage exceptional candidates in competitive and geographically distributed markets.
Own searches from start to finish. Drive recruiting processes across both specialized leadership positions and high-priority operational roles, from initial sourcing through offer acceptance.
Support hiring across multiple locations. Develop recruiting strategies tailored to the regional labor markets surrounding our current and future data center sites.
Help scale the Talent Acquisition function. Improve sourcing strategies, hiring workflows, and candidate experience as Gimlet expands its infrastructure footprint.
You may be a good fit if you have
5+ years of recruiting experience, with a track record of hiring technical, infrastructure, or operations talent in competitive markets.
Experience recruiting across data center operations, critical facilities, infrastructure engineering, construction, site development, capacity delivery, or related disciplines.
Strong sourcing skills, with demonstrated success identifying, engaging, and closing both specialized professionals and operational talent.
An understanding of the data center ecosystem and the companies, teams, and technical backgrounds relevant to large-scale infrastructure development.
Strong judgment and the ability to partner closely with infrastructure and operations leaders on hiring priorities, candidate evaluation, and market dynamics.
High ownership and the ability to independently manage multiple searches across different functions, seniority levels, and geographic markets.
Strong candidates may also have
Direct recruiting experience at hyperscalers, neocloud providers, colocation companies, or large-scale data center operators.
Experience hiring data center technicians, critical facilities engineers, site sourcing professionals, construction managers, or infrastructure operations leaders.
Established relationships within data center, power, construction, or infrastructure development communities.
Experience scaling teams across multiple data center locations or supporting rapid infrastructure expansion at a high-growth company.
Why join now?
Gimlet is expanding from its core technology into a production neocloud spanning new hardware, customers, and data centers.
Solve hard problems.
Own meaningful work.
Build for production.
Help define what’s next.
Agency Policy: Gimlet Labs does not accept unsolicited resumes from recruitment agencies or search firms. Any unsolicited resumes submitted without a signed agreement will be considered the property of Gimlet Labs, and no fees will be paid.
","descriptionPlain":"About us\n\nGimlet is building the first multi-silicon neocloud designed for fast, efficient AI inference.\n\nWe combine large-scale compute infrastructure with an execution platform that partitions AI workloads and maps each stage to the hardware best suited to run it.\n\nWe work with foundation labs, hyperscalers, and AI-native companies, giving our team access to technical problems spanning frontier models, production infrastructure, and emerging hardware.\n\nAbout the role \n\nGimlet Labs is hiring a Senior Technical Recruiter to help build the teams responsible for expanding and operating our next generation of AI data centers.\n\nAs demand for AI compute accelerates, securing power, delivering capacity, and operating reliable infrastructure at scale are increasingly critical challenges. We're building the teams that turn ambitious infrastructure plans into operational data centers, from site sourcing and capacity development to engineering, deployment, and ongoing operations.\n\nYou will partner directly with infrastructure and operations leadership to identify exceptional talent across highly specialized disciplines, develop creative sourcing strategies, and build relationships with the people who know how to deliver complex infrastructure at scale.\n\nWe are looking for someone who loves the craft of recruiting, understands the value of deep industry relationships, and is excited to help build the physical infrastructure powering the next generation of AI.\n\nWhat success looks like\n\nIn your first 6–12 months, you will:\n\n - Build exceptional infrastructure teams. Identify, engage, and close talent across data center operations, critical facilities, site sourcing, capacity delivery, infrastructure engineering, and technical operations.\n\n - Become a trusted partner to infrastructure leadership. Understand our expansion priorities, technical requirements, and operational challenges to help hiring managers make informed decisions about talent and hiring strategy.\n\n - Build differentiated talent pipelines. Map relevant companies, data center operators, hyperscalers, and infrastructure teams to identify and engage exceptional candidates in competitive and geographically distributed markets.\n\n - Own searches from start to finish. Drive recruiting processes across both specialized leadership positions and high-priority operational roles, from initial sourcing through offer acceptance.\n\n - Support hiring across multiple locations. Develop recruiting strategies tailored to the regional labor markets surrounding our current and future data center sites.\n\n - Help scale the Talent Acquisition function. Improve sourcing strategies, hiring workflows, and candidate experience as Gimlet expands its infrastructure footprint.\n\nYou may be a good fit if you have\n\n - 5+ years of recruiting experience, with a track record of hiring technical, infrastructure, or operations talent in competitive markets.\n\n - Experience recruiting across data center operations, critical facilities, infrastructure engineering, construction, site development, capacity delivery, or related disciplines.\n\n - Strong sourcing skills, with demonstrated success identifying, engaging, and closing both specialized professionals and operational talent.\n\n - An understanding of the data center ecosystem and the companies, teams, and technical backgrounds relevant to large-scale infrastructure development.\n\n - Strong judgment and the ability to partner closely with infrastructure and operations leaders on hiring priorities, candidate evaluation, and market dynamics.\n\n - High ownership and the ability to independently manage multiple searches across different functions, seniority levels, and geographic markets.\n\nStrong candidates may also have\n\n - Direct recruiting experience at hyperscalers, neocloud providers, colocation companies, or large-scale data center operators.\n\n - Experience hiring data center technicians, critical facilities engineers, site sourcing professionals, construction managers, or infrastructure operations leaders.\n\n - Established relationships within data center, power, construction, or infrastructure development communities.\n\n - Experience scaling teams across multiple data center locations or supporting rapid infrastructure expansion at a high-growth company.\n\nWhy join now?\n\nGimlet is expanding from its core technology into a production neocloud spanning new hardware, customers, and data centers.\n\n - Solve hard problems.\n\n - Own meaningful work.\n\n - Build for production.\n\n - Help define what’s next.\n\nAgency Policy: Gimlet Labs does not accept unsolicited resumes from recruitment agencies or search firms. Any unsolicited resumes submitted without a signed agreement will be considered the property of Gimlet Labs, and no fees will be paid."},{"id":"12b8c281-a4ea-4f86-ab80-a258b4e26d79","title":"Strategic Hardware Partnerships","department":"GTM","team":"Sales","employmentType":"FullTime","location":"San Francisco, CA","secondaryLocations":[],"publishedAt":"2026-10-13T22:05:00.622+00:00","isListed":true,"isRemote":false,"workplaceType":"OnSite","address":{"postalAddress":{"postalCode":"94103","addressRegion":"California","streetAddress":"255 Portrero Ave","addressCountry":"United States","addressLocality":"San Francisco"}},"jobUrl":"https://jobs.ashbyhq.com/gimlet/12b8c281-a4ea-4f86-ab80-a258b4e26d79","applyUrl":"https://jobs.ashbyhq.com/gimlet/12b8c281-a4ea-4f86-ab80-a258b4e26d79/application","descriptionHtml":"About us
Gimlet is building the first multi-silicon neocloud designed for fast, efficient AI inference.
We combine large-scale compute infrastructure with an execution platform that partitions AI workloads and maps each stage to the hardware best suited to run it.
We work with foundation labs, hyperscalers, and AI-native companies, giving our team access to technical problems spanning frontier models, production infrastructure, and emerging hardware.
About the role
As part of Strategic Hardware Partnerships, you will turn Gimlet Labs’ relationships with established silicon providers and emerging accelerator companies into deployed technical and economic advantage. Gimlet is building a heterogeneous AI cloud designed to run workloads on the hardware best suited to them. You will independently own major partners and programs end to end, from partner strategy and technical evaluation through commercial agreements, POCs, deployment and scale.
This is an operator-first role: you will lead cross-functional teams and drive outcomes, combining strategic judgment with hands-on execution. You will own the partner relationship, partner strategy and commercial position, including financing structures, capacity commitments and roadmap alignment. You will work closely with GTM, Supply Chain, Engineering, Finance, Legal, Infrastructure and PMO; functional teams retain ownership of their domains while you integrate the work and remain accountable for the partner outcome.
We can flex the level and scope of this role for the right person. We care most about your ability to take accountability for complex partnerships, make sound decisions and get important work done.
What success looks like
In the first 12-18 months, you will:
Build and execute strategies for major and emerging silicon and accelerator partners, grounded in Gimlet’s workload needs, performance, supply, economics and scale
Establish trusted relationships with partner executives and working teams, securing roadmap access and clear mutual commitments
Lead cross-functional partner programs with clear owners, decisions and dependencies across Engineering, GTM, Supply Chain, Finance, Legal, Infrastructure and PMO
Drive technical evaluations and POCs with Engineering, defining success criteria and translating results into partner and deployment decisions
Develop and negotiate partner-side commercial positions, including pricing, financing structures, capacity commitments and agreements, with Finance and Legal
Work with GTM to connect partner capabilities to customer demand, offtake and joint go-to-market opportunities, including three-party programs
Carry partner commitments through hardware supply, deployment and production scale with Supply Chain and Infrastructure, resolving risks and escalations
Maintain an integrated view of each program’s technical, commercial and operational progress, and turn decisions into measurable partner outcomes
You may be a good fit if you have
A track record of owning complex partnerships or programs and delivering results through hands-on execution
Strong commercial judgment and the ability to develop a negotiating position across economics, financing, capacity and risk
The ability to lead cross-functional teams and influence senior stakeholders without relying on reporting lines
Enough technical fluency to work effectively with Engineering on silicon, accelerators, AI workloads and infrastructure tradeoffs
The ability to move between long-term partner strategy and the details of POCs, agreements, supply and deployment
High agency, practical problem-solving skills and persistent follow-through in a fast-moving environment
Clear written and verbal communication, including the ability to turn complex issues into decisions and commitments
Sound judgment, a strong sense of ownership and the ability to build trusted relationships while handling sensitive commercial information
Strong candidates may also have
Experience with established silicon providers, emerging accelerator companies, hardware ecosystems or AI infrastructure
Experience taking a partner program from evaluation or POC into deployment and production scale
Experience structuring hardware or capacity agreements, financing arrangements or other complex commercial transactions
Experience connecting hardware partner programs to customer demand, offtake or joint go-to-market activity
Experience working across hardware supply chains, cloud infrastructure and technical integration in a growing company
Why join now?
Gimlet is expanding from its core technology into a production neocloud spanning new hardware, customers, and data centers.
Solve hard problems.
Own meaningful work.
Build for production.
Help define what’s next.
Agency Policy: Gimlet Labs does not accept unsolicited resumes from recruitment agencies or search firms. Any unsolicited resumes submitted without a signed agreement will be considered the property of Gimlet Labs, and no fees will be paid.
","descriptionPlain":"About us\n\nGimlet is building the first multi-silicon neocloud designed for fast, efficient AI inference.\n\nWe combine large-scale compute infrastructure with an execution platform that partitions AI workloads and maps each stage to the hardware best suited to run it.\n\nWe work with foundation labs, hyperscalers, and AI-native companies, giving our team access to technical problems spanning frontier models, production infrastructure, and emerging hardware.\n\nAbout the role\n\nAs part of Strategic Hardware Partnerships, you will turn Gimlet Labs’ relationships with established silicon providers and emerging accelerator companies into deployed technical and economic advantage. Gimlet is building a heterogeneous AI cloud designed to run workloads on the hardware best suited to them. You will independently own major partners and programs end to end, from partner strategy and technical evaluation through commercial agreements, POCs, deployment and scale.\n\nThis is an operator-first role: you will lead cross-functional teams and drive outcomes, combining strategic judgment with hands-on execution. You will own the partner relationship, partner strategy and commercial position, including financing structures, capacity commitments and roadmap alignment. You will work closely with GTM, Supply Chain, Engineering, Finance, Legal, Infrastructure and PMO; functional teams retain ownership of their domains while you integrate the work and remain accountable for the partner outcome.\n\nWe can flex the level and scope of this role for the right person. We care most about your ability to take accountability for complex partnerships, make sound decisions and get important work done.\n\nWhat success looks like\n\nIn the first 12-18 months, you will:\n\n - Build and execute strategies for major and emerging silicon and accelerator partners, grounded in Gimlet’s workload needs, performance, supply, economics and scale\n\n - Establish trusted relationships with partner executives and working teams, securing roadmap access and clear mutual commitments\n\n - Lead cross-functional partner programs with clear owners, decisions and dependencies across Engineering, GTM, Supply Chain, Finance, Legal, Infrastructure and PMO\n\n - Drive technical evaluations and POCs with Engineering, defining success criteria and translating results into partner and deployment decisions\n\n - Develop and negotiate partner-side commercial positions, including pricing, financing structures, capacity commitments and agreements, with Finance and Legal\n\n - Work with GTM to connect partner capabilities to customer demand, offtake and joint go-to-market opportunities, including three-party programs\n\n - Carry partner commitments through hardware supply, deployment and production scale with Supply Chain and Infrastructure, resolving risks and escalations\n\n - Maintain an integrated view of each program’s technical, commercial and operational progress, and turn decisions into measurable partner outcomes\n\nYou may be a good fit if you have\n\n - A track record of owning complex partnerships or programs and delivering results through hands-on execution\n\n - Strong commercial judgment and the ability to develop a negotiating position across economics, financing, capacity and risk\n\n - The ability to lead cross-functional teams and influence senior stakeholders without relying on reporting lines\n\n - Enough technical fluency to work effectively with Engineering on silicon, accelerators, AI workloads and infrastructure tradeoffs\n\n - The ability to move between long-term partner strategy and the details of POCs, agreements, supply and deployment\n\n - High agency, practical problem-solving skills and persistent follow-through in a fast-moving environment\n\n - Clear written and verbal communication, including the ability to turn complex issues into decisions and commitments\n\n - Sound judgment, a strong sense of ownership and the ability to build trusted relationships while handling sensitive commercial information\n\nStrong candidates may also have\n\n - Experience with established silicon providers, emerging accelerator companies, hardware ecosystems or AI infrastructure\n\n - Experience taking a partner program from evaluation or POC into deployment and production scale\n\n - Experience structuring hardware or capacity agreements, financing arrangements or other complex commercial transactions\n\n - Experience connecting hardware partner programs to customer demand, offtake or joint go-to-market activity\n\n - Experience working across hardware supply chains, cloud infrastructure and technical integration in a growing company\n\nWhy join now?\n\nGimlet is expanding from its core technology into a production neocloud spanning new hardware, customers, and data centers.\n\n - Solve hard problems.\n\n - Own meaningful work.\n\n - Build for production.\n\n - Help define what’s next.\n\nAgency Policy: Gimlet Labs does not accept unsolicited resumes from recruitment agencies or search firms. Any unsolicited resumes submitted without a signed agreement will be considered the property of Gimlet Labs, and no fees will be paid."},{"id":"f7fb0cae-f7d9-427c-b6a1-aa6686bf5882","title":"Project Coordinator, Hardware Partnerships","department":"GTM","team":"Sales","employmentType":"FullTime","location":"San Francisco, CA","secondaryLocations":[],"publishedAt":"2026-10-12T22:24:21.019+00:00","isListed":true,"isRemote":false,"workplaceType":"OnSite","address":{"postalAddress":{"postalCode":"94103","addressRegion":"California","streetAddress":"255 Portrero Ave","addressCountry":"United States","addressLocality":"San Francisco"}},"jobUrl":"https://jobs.ashbyhq.com/gimlet/f7fb0cae-f7d9-427c-b6a1-aa6686bf5882","applyUrl":"https://jobs.ashbyhq.com/gimlet/f7fb0cae-f7d9-427c-b6a1-aa6686bf5882/application","descriptionHtml":"About us
Gimlet is building the first multi-silicon neocloud designed for fast, efficient AI inference.
We combine large-scale compute infrastructure with an execution platform that partitions AI workloads and maps each stage to the hardware best suited to run it.
We work with foundation labs, hyperscalers, and AI-native companies, giving our team access to technical problems spanning frontier models, production infrastructure, and emerging hardware.
About the role
As Project Coordinator, Hardware Partnerships, you will be a force multiplier for Gimlet Labs’ Hardware Partnerships team. Gimlet is building a heterogeneous AI cloud, and its hardware relationships span technical evaluations, POCs, commercial negotiations, financing, supply and deployment. You will help the team keep those parallel threads organized and moving.
You will work closely with the Hardware Partnerships lead and partnership owners to prepare partner meetings, maintain Notion pages and trackers, capture actions and commitments, and follow through across Engineering, GTM, Supply Chain, Finance, Legal, Infrastructure and PMO. Partnership owners lead strategy and own the overall partner outcome; you make them more effective by keeping information, milestones and dependencies clear. As you build context and judgment, you may take ownership of smaller workstreams directly.
Exceptional organization, follow-through, judgment and learning agility matter more than a specific title or years of project-management experience.
What success looks like
In the first 12-18 months, you will:
Prepare partner meetings with current context, open items, decisions needed and required inputs so the team can use meeting time effectively
Capture actions, decisions and commitments with clear owners and deadlines, and follow through until they are resolved
Keep Notion partnership pages, trackers and project information accurate, current and easy to use across the portfolio
Track technical evaluations, POCs, commercial workstreams and deployment milestones, keeping progress and upcoming commitments visible
Coordinate dependencies and deliverables across Engineering, GTM, Supply Chain, Finance, Legal, Infrastructure, PMO and partner teams
Surface risks, blockers, overdue actions and decisions early, using judgment about what to resolve directly and what to escalate
Build lightweight status updates and repeatable operating rhythms that reduce the time partnership owners spend reconstructing context and chasing follow-ups
Take ownership of smaller projects or work streams as your context and capability grow, with clear scope and accountability
You may be a good fit if you have
Exceptional organization and follow-through, with the ability to manage multiple parallel threads
High attention to detail while keeping the most important priorities in view
Strong written and verbal communication, including the ability to turn messy discussions into clear actions and concise updates
Sound judgment and discretion, including knowing when to act independently and when to escalate
The ability to adjust quickly as priorities, information and deadlines change
High agency and a proactive approach to noticing gaps, solving problems and closing the loop
Learning agility and curiosity, with the ability to build context across technical, commercial and operational topics
Comfort working across functions and following up persistently and professionally with busy internal and external stakeholders
Strong candidates may also have
Experience in project or program coordination, team operations or executive support
Experience maintaining Notion or similar collaboration and project-tracking tools
Experience in hardware, AI infrastructure, cloud or another technology company
Experience supporting technical teams or coordinating complex cross-functional projects
Experience in partner- or customer-facing work, including meeting preparation and tracking external commitments
Why join now?
Gimlet is expanding from its core technology into a production neocloud spanning new hardware, customers, and data centers.
Solve hard problems.
Own meaningful work.
Build for production.
Help define what’s next.
Agency Policy: Gimlet Labs does not accept unsolicited resumes from recruitment agencies or search firms. Any unsolicited resumes submitted without a signed agreement will be considered the property of Gimlet Labs, and no fees will be paid.
","descriptionPlain":"About us\n\nGimlet is building the first multi-silicon neocloud designed for fast, efficient AI inference.\n\nWe combine large-scale compute infrastructure with an execution platform that partitions AI workloads and maps each stage to the hardware best suited to run it.\n\nWe work with foundation labs, hyperscalers, and AI-native companies, giving our team access to technical problems spanning frontier models, production infrastructure, and emerging hardware.\n\nAbout the role\n\nAs Project Coordinator, Hardware Partnerships, you will be a force multiplier for Gimlet Labs’ Hardware Partnerships team. Gimlet is building a heterogeneous AI cloud, and its hardware relationships span technical evaluations, POCs, commercial negotiations, financing, supply and deployment. You will help the team keep those parallel threads organized and moving.\n\nYou will work closely with the Hardware Partnerships lead and partnership owners to prepare partner meetings, maintain Notion pages and trackers, capture actions and commitments, and follow through across Engineering, GTM, Supply Chain, Finance, Legal, Infrastructure and PMO. Partnership owners lead strategy and own the overall partner outcome; you make them more effective by keeping information, milestones and dependencies clear. As you build context and judgment, you may take ownership of smaller workstreams directly.\n\nExceptional organization, follow-through, judgment and learning agility matter more than a specific title or years of project-management experience.\n\nWhat success looks like\n\nIn the first 12-18 months, you will:\n\n - Prepare partner meetings with current context, open items, decisions needed and required inputs so the team can use meeting time effectively\n\n - Capture actions, decisions and commitments with clear owners and deadlines, and follow through until they are resolved\n\n - Keep Notion partnership pages, trackers and project information accurate, current and easy to use across the portfolio\n\n - Track technical evaluations, POCs, commercial workstreams and deployment milestones, keeping progress and upcoming commitments visible\n\n - Coordinate dependencies and deliverables across Engineering, GTM, Supply Chain, Finance, Legal, Infrastructure, PMO and partner teams\n\n - Surface risks, blockers, overdue actions and decisions early, using judgment about what to resolve directly and what to escalate\n\n - Build lightweight status updates and repeatable operating rhythms that reduce the time partnership owners spend reconstructing context and chasing follow-ups\n\n - Take ownership of smaller projects or work streams as your context and capability grow, with clear scope and accountability\n\nYou may be a good fit if you have\n\n - Exceptional organization and follow-through, with the ability to manage multiple parallel threads\n\n - High attention to detail while keeping the most important priorities in view\n\n - Strong written and verbal communication, including the ability to turn messy discussions into clear actions and concise updates\n\n - Sound judgment and discretion, including knowing when to act independently and when to escalate\n\n - The ability to adjust quickly as priorities, information and deadlines change\n\n - High agency and a proactive approach to noticing gaps, solving problems and closing the loop\n\n - Learning agility and curiosity, with the ability to build context across technical, commercial and operational topics\n\n - Comfort working across functions and following up persistently and professionally with busy internal and external stakeholders\n\nStrong candidates may also have\n\n - Experience in project or program coordination, team operations or executive support\n\n - Experience maintaining Notion or similar collaboration and project-tracking tools\n\n - Experience in hardware, AI infrastructure, cloud or another technology company\n\n - Experience supporting technical teams or coordinating complex cross-functional projects\n\n - Experience in partner- or customer-facing work, including meeting preparation and tracking external commitments\n\n\n\n\nWhy join now?\n\nGimlet is expanding from its core technology into a production neocloud spanning new hardware, customers, and data centers.\n\n - Solve hard problems.\n\n - Own meaningful work.\n\n - Build for production.\n\n - Help define what’s next.\n\nAgency Policy: Gimlet Labs does not accept unsolicited resumes from recruitment agencies or search firms. Any unsolicited resumes submitted without a signed agreement will be considered the property of Gimlet Labs, and no fees will be paid."}],"apiVersion":"1"}