As a Member of Technical Staff (MTS - Staff Engineer) within the company, you will own the architecture, reliability, and evolution of Collinear’s AI agent improvement product offerings. This role is deeply hands-on and focused on backend systems, infrastructure, and distributed execution, with a strong emphasis on performance, scalability, and enterprise readiness.
You will be responsible for designing and operating the systems that support multiple customers, multiple deployment models, and an expanding set of evaluation and simulation scenarios. While people management and hiring are nice to have, the priority is technical leadership, architectural judgment, and execution in a fast-moving, customer-facing environment.
This role is ideal for someone who has built and operated cloud services at scale and wants to apply that experience to a complex, high-impact AI platform.
Own the architecture, reliability, and scalability of Collinear’s AI agent improvement product offerings.
Design and operate multi-tenant, distributed backend systems that support enterprise customers.
Make platform-level decisions across infrastructure, runtimes, deployment models, and developer experience.
Translate customer needs and ambiguity into clear engineering priorities and durable platform capabilities.
Partner with founders and research leadership on technical strategy, roadmap, and tradeoffs.
Establish engineering standards and best practices as the platform and team scale.
Hire and mentor engineers over time as the organization grows.
We’re looking for someone who is deeply technical, product-minded, and comfortable operating with ambiguity.
You likely have:
7–10+ years of experience building backend systems, platforms, or cloud services.
Strong experience with distributed systems, infrastructure, and cloud-native architectures.
Hands-on experience with:
Python (primary language today)
Containers and orchestration (Docker, Kubernetes)
Cloud platforms (AWS, GCP, or Azure)
Experience building or operating B2B SaaS platforms at scale, including multitenancy and reliability concerns.
Comfort working directly with customers in a B2B setting, including handling escalations and ambiguous requirements.
Strong communication skills and sound technical judgment.
Experience in cloud or data infrastructure companies (e.g., AWS, GCP, Azure, Databricks, Snowflake, Datadog, Confluent).
Prior experience at an early-stage startup.
Some people management or hiring experience (not required).
Experience working on ML platforms, evaluation systems, or large-scale data pipelines.
Collinear is an equal opportunity employer and values diversity. We do not discriminate on the basis of race, color, religion, sex, gender identity, sexual orientation, national origin, age, disability, veteran status, or any other characteristic protected by applicable law.
The base salary range for this role in California is $200,000 to $400,000 per year, depending on experience, skills, and qualifications. This role will also be eligible for equity, benefits, and bonuses.
Collinear provides reasonable accommodations for candidates with disabilities throughout the application and hiring process. If you need an accommodation, please contact us.
Pursuant to applicable local ordinances, we will consider qualified applicants with arrest and conviction records.
","descriptionPlain":"ABOUT THE ROLE\n\nAs a Member of Technical Staff (MTS - Staff Engineer) within the company, you will own the architecture, reliability, and evolution of Collinear’s AI agent improvement product offerings. This role is deeply hands-on and focused on backend systems, infrastructure, and distributed execution, with a strong emphasis on performance, scalability, and enterprise readiness.\n\nYou will be responsible for designing and operating the systems that support multiple customers, multiple deployment models, and an expanding set of evaluation and simulation scenarios. While people management and hiring are nice to have, the priority is technical leadership, architectural judgment, and execution in a fast-moving, customer-facing environment.\n\nThis role is ideal for someone who has built and operated cloud services at scale and wants to apply that experience to a complex, high-impact AI platform.\n\n\n\n\nWHAT YOU’LL DO\n\n - Own the architecture, reliability, and scalability of Collinear’s AI agent improvement product offerings.\n\n - Design and operate multi-tenant, distributed backend systems that support enterprise customers.\n\n - Make platform-level decisions across infrastructure, runtimes, deployment models, and developer experience.\n\n - Translate customer needs and ambiguity into clear engineering priorities and durable platform capabilities.\n\n - Partner with founders and research leadership on technical strategy, roadmap, and tradeoffs.\n\n - Establish engineering standards and best practices as the platform and team scale.\n\n - Hire and mentor engineers over time as the organization grows.\n \n \n\n\nABOUT YOU\n\nWe’re looking for someone who is deeply technical, product-minded, and comfortable operating with ambiguity.\n\nYou likely have:\n\n - 7–10+ years of experience building backend systems, platforms, or cloud services.\n\n - Strong experience with distributed systems, infrastructure, and cloud-native architectures.\n\n - Hands-on experience with:\n \n - Python (primary language today)\n \n - Containers and orchestration (Docker, Kubernetes)\n \n - Cloud platforms (AWS, GCP, or Azure)\n\n - Experience building or operating B2B SaaS platforms at scale, including multitenancy and reliability concerns.\n\n - Comfort working directly with customers in a B2B setting, including handling escalations and ambiguous requirements.\n\n - Strong communication skills and sound technical judgment.\n \n \n\n\nNICE TO HAVE\n\n - Experience in cloud or data infrastructure companies (e.g., AWS, GCP, Azure, Databricks, Snowflake, Datadog, Confluent).\n\n - Prior experience at an early-stage startup.\n\n - Some people management or hiring experience (not required).\n\n - Experience working on ML platforms, evaluation systems, or large-scale data pipelines.\n\nCollinear is an equal opportunity employer and values diversity. We do not discriminate on the basis of race, color, religion, sex, gender identity, sexual orientation, national origin, age, disability, veteran status, or any other characteristic protected by applicable law.\n\nThe base salary range for this role in California is $200,000 to $400,000 per year, depending on experience, skills, and qualifications. This role will also be eligible for equity, benefits, and bonuses.\n\nCollinear provides reasonable accommodations for candidates with disabilities throughout the application and hiring process. If you need an accommodation, please contact us.\n\nPursuant to applicable local ordinances, we will consider qualified applicants with arrest and conviction records.\n\n"},{"id":"72bb3260-8663-4fe3-ae62-cefaac59ba1c","title":"MTS - Engineering","department":"Engineering","team":"Engineering","employmentType":"FullTime","location":"Sunnyvale, California","secondaryLocations":[{"location":"San Francisco, CA","address":{"postalAddress":{"postalCode":"94105","addressRegion":"California","streetAddress":"","addressCountry":"United States","addressLocality":"San Francisco"}}}],"publishedAt":"2026-05-01T22:09:15.759+00:00","isListed":true,"isRemote":true,"workplaceType":"Hybrid","address":{"postalAddress":{"addressCountry":"United States"}},"jobUrl":"https://jobs.ashbyhq.com/collinear-ai/72bb3260-8663-4fe3-ae62-cefaac59ba1c","applyUrl":"https://jobs.ashbyhq.com/collinear-ai/72bb3260-8663-4fe3-ae62-cefaac59ba1c/application","descriptionHtml":"At Collinear, we help teams fearlessly ship AI.
Frontier labs and AI-native companies use our SimLab to find capability gaps in their agents and generate high-quality data to close them. We believe that the next generation of AI progress won't come from just bigger models, but from more rigorous, long-horizon simulation and programmatic verification.
SimLab allows researchers to spin up realistic environments, run agents through complex tasks, and surface failure modes under real-world conditions. We then close the loop by generating targeted synthetic data to retrain models, delivering measurable quality lift on the metrics that actually matter.
We are looking for a talented Member of Technical Staff (MTS - Engineering) with expertise in React and NextJs (JavaScript frameworks) for frontend development and backend development in Python and Fast API. The ideal candidate will have hands-on experience in DevOps technologies, testing frameworks, database management, and exposure to AI/ML or NLP/LLM projects.
Key responsibilities include:
Develop scalable, responsive web applications using modern frontend frameworks (React/Next.js)
Design and implement high-performance backend solutions using Python and FastAPI, ensuring reliability and scalability
Collaborate with cross-functional teams to define features, enhancements, and deliver product updates
Implement and maintain DevOps best practices for continuous integration and deployment using tools like Jenkins, AWS, Docker, and Kubernetes
Write and maintain comprehensive unit, integration, and end-to-end tests using testing frameworks
Troubleshoot and debug frontend and backend issues, ensuring timely resolution and system optimization.
Work with both SQL and NoSQL databases, optimizing queries for efficient data management
Collaborate with AI/ML teams to build and deploy applications leveraging NLP and LLM technologies
There are a few specific things we’ll be looking for that will help you succeed in this role:
Bachelor’s or Master’s degree in Computer Science/Engineering, or a related field
Experience in full stack development with a focus on both frontend and backend technologies
Proficiency in JavaScript frameworks (React/Next.js) for frontend development
Strong backend development skills in Python (FastAPI) or similar languages
Experience with DevOps tools such as Jenkins, AWS, Docker, and Kubernetes for CI/CD pipelines
Hands-on experience with testing frameworks and a Test-Driven Development (TDD) approach
Expertise in SQL and NoSQL databases, with a solid understanding of database design and optimization
Experience in AI/ML or NLP/LLM projects is highly desirable
Contributions to open-source projects, with an active GitHub portfolio showcasing innovation and expertise, are preferred
Strong problem-solving skills and the ability to work in a fast-paced, collaborative environment
Prior experience in top-tier technology companies or startups is a plus
Collinear is an equal opportunity employer and values diversity. We do not discriminate on the basis of race, color, religion, sex, gender identity, sexual orientation, national origin, age, disability, veteran status, or any other characteristic protected by applicable law.
The base salary range for this role in California is $150,000 to $300,000 per year, depending on experience, skills, and qualifications. This role will also be eligible for equity, benefits, and bonuses.
Collinear provides reasonable accommodations for candidates with disabilities throughout the application and hiring process. If you need an accommodation, please contact us.
Pursuant to applicable local ordinances, we will consider qualified applicants with arrest and conviction records.
","descriptionPlain":"ABOUT COLLINEAR\n\nAt Collinear, we help teams fearlessly ship AI.\n\nFrontier labs and AI-native companies use our SimLab to find capability gaps in their agents and generate high-quality data to close them. We believe that the next generation of AI progress won't come from just bigger models, but from more rigorous, long-horizon simulation and programmatic verification.\n\nSimLab allows researchers to spin up realistic environments, run agents through complex tasks, and surface failure modes under real-world conditions. We then close the loop by generating targeted synthetic data to retrain models, delivering measurable quality lift on the metrics that actually matter.\n\n\n\n\nABOUT THE ROLE\n\nWe are looking for a talented Member of Technical Staff (MTS - Engineering) with expertise in React and NextJs (JavaScript frameworks) for frontend development and backend development in Python and Fast API. The ideal candidate will have hands-on experience in DevOps technologies, testing frameworks, database management, and exposure to AI/ML or NLP/LLM projects.\n\nKey responsibilities include:\n\n - Develop scalable, responsive web applications using modern frontend frameworks (React/Next.js)\n\n - Design and implement high-performance backend solutions using Python and FastAPI, ensuring reliability and scalability\n\n - Collaborate with cross-functional teams to define features, enhancements, and deliver product updates\n\n - Implement and maintain DevOps best practices for continuous integration and deployment using tools like Jenkins, AWS, Docker, and Kubernetes\n\n - Write and maintain comprehensive unit, integration, and end-to-end tests using testing frameworks\n\n - Troubleshoot and debug frontend and backend issues, ensuring timely resolution and system optimization.\n\n - Work with both SQL and NoSQL databases, optimizing queries for efficient data management\n\n - Collaborate with AI/ML teams to build and deploy applications leveraging NLP and LLM technologies\n\n\nABOUT YOU\n\nThere are a few specific things we’ll be looking for that will help you succeed in this role:\n\n - Bachelor’s or Master’s degree in Computer Science/Engineering, or a related field \n\n - Experience in full stack development with a focus on both frontend and backend technologies\n\n - Proficiency in JavaScript frameworks (React/Next.js) for frontend development\n\n - Strong backend development skills in Python (FastAPI) or similar languages\n\n - Experience with DevOps tools such as Jenkins, AWS, Docker, and Kubernetes for CI/CD pipelines\n\n - Hands-on experience with testing frameworks and a Test-Driven Development (TDD) approach\n\n - Expertise in SQL and NoSQL databases, with a solid understanding of database design and optimization\n\n - Experience in AI/ML or NLP/LLM projects is highly desirable\n\n - Contributions to open-source projects, with an active GitHub portfolio showcasing innovation and expertise, are preferred\n\n - Strong problem-solving skills and the ability to work in a fast-paced, collaborative environment\n\n - Prior experience in top-tier technology companies or startups is a plus\n\n\n\nCollinear is an equal opportunity employer and values diversity. We do not discriminate on the basis of race, color, religion, sex, gender identity, sexual orientation, national origin, age, disability, veteran status, or any other characteristic protected by applicable law.\n\nThe base salary range for this role in California is $150,000 to $300,000 per year, depending on experience, skills, and qualifications. This role will also be eligible for equity, benefits, and bonuses.\n\nCollinear provides reasonable accommodations for candidates with disabilities throughout the application and hiring process. If you need an accommodation, please contact us.\n\nPursuant to applicable local ordinances, we will consider qualified applicants with arrest and conviction records."},{"id":"4b21f4d6-519c-42c9-b721-e73bbc81b3ad","title":"MTS - Research","department":"Research","team":"Research","employmentType":"FullTime","location":"Sunnyvale, California","secondaryLocations":[{"location":"San Francisco, CA","address":{"postalAddress":{"postalCode":"94105","addressRegion":"California","streetAddress":"","addressCountry":"United States","addressLocality":"San Francisco"}}}],"publishedAt":"2026-04-30T17:43:08.385+00:00","isListed":true,"isRemote":false,"workplaceType":"OnSite","address":{"postalAddress":{"addressCountry":"United States"}},"jobUrl":"https://jobs.ashbyhq.com/collinear-ai/4b21f4d6-519c-42c9-b721-e73bbc81b3ad","applyUrl":"https://jobs.ashbyhq.com/collinear-ai/4b21f4d6-519c-42c9-b721-e73bbc81b3ad/application","descriptionHtml":"At Collinear, we help teams fearlessly ship AI.
We are looking for MTS - Research team member to help us build the data engine for frontier AI. You will develop the high-fidelity environments and evaluation stacks that the world’s leading AI labs rely on to stress-test their most advanced agents. You will work across domains including Computer Use, Enterprise MCP/Toolcalling and Coding. Verifier Design, Simulated Personas, Benchmarking Personal AGI are some of the research areas we work on.
Build Agentic Environments: Design and implement the next generation of \"SimLabs\", ultra-realistic, long-horizon simulation environments where agents learn to navigate ambiguity and maintain context.
Programmatic and Agentic Verification: Develop rigorous, policy-aware judges and evaluations that measure genuine capability and safety beyond simple benchmarks.
Close the Loop: Design and execute high-quality post-training runs (CPT, SFT, RL) to deliver frontier performance on open-source models using curated, high-signal data.
Collaborate: Work daily with the founders and research staff to shape the roadmap and push the state-of-the-art in AI reliability.
Work on analyzing model failure modes and creating frontier data pipelines which scale with test-time compute.
We are looking for individuals who demonstrate a rare combination of technical depth, research intuition, and high agency.
Technical Foundation: A Bachelor’s, Master’s, or PhD in a technical field (CS, Math, Physics, etc.), or a demonstrated \"proof of work\" through significant open-source contributions or industry experience.
Engineering Rigor: A strong foundation in software engineering with the ability to build robust, scalable infrastructure. You should be comfortable in a Python-friendly, CLI-first development environment.
ML Fluency: A principled understanding of foundation models, including how they are constructed, evaluated, and optimized.
Empirical Mindset: Experience conducting research or technical experiments with a focus on reproducibility and data-driven results.
Research Taste: You have a strong intuition for identifying what matters in complex problem spaces. You can balance deep research exploration with the pragmatism needed to ship a product.
Impact-Driven Agency: You care about outcomes, not just activity. You don't wait for a ticket; you identify gaps in the system, build the solution, and ensure it moves real-world metrics for frontier AI labs.
Domain Expertise: Prior experience with Reinforcement Learning (RLHF/RLAIF), simulation systems, or building long-horizon agentic environments.
Proven Track Record: A history of contributing to influential ML research (e.g., publications at NeurIPS, ICLR, ICML) or maintaining high-impact open-source projects.
Post-Training Experience: Experience fine-tuning or evaluating large-scale models to deliver \"frontier performance\" on open-source benchmarks.
Own the Frontier: Work on the most pressing problem in AI today: making agents reliable enough for production.
High Density of Talent: Join a small, elite team where you will be pushed to do your life's work.
Elite Compensation: We offer competitive salary and equity packages to ensure we attract the best of the best.
Direct Impact: At a seed-backed startup, your work directly shapes the company's trajectory and the future of AI safety.
Collinear is an equal opportunity employer and values diversity. We do not discriminate on the basis of race, color, religion, sex, gender identity, sexual orientation, national origin, age, disability, veteran status, or any other characteristic protected by applicable law.
The base salary range for this role in California is $160,000 to $400,000 per year, depending on experience, skills, and qualifications. This role will also be eligible for equity, benefits, and bonuses.
Collinear provides reasonable accommodations for candidates with disabilities throughout the application and hiring process. If you need an accommodation, please contact us.
Pursuant to applicable local ordinances, we will consider qualified applicants with arrest and conviction records.
","descriptionPlain":"ABOUT COLLINEAR\n\nAt Collinear, we help teams fearlessly ship AI.\n\n\n\n\nABOUT THE ROLE\n\nWe are looking for MTS - Research team member to help us build the data engine for frontier AI. You will develop the high-fidelity environments and evaluation stacks that the world’s leading AI labs rely on to stress-test their most advanced agents. You will work across domains including Computer Use, Enterprise MCP/Toolcalling and Coding. Verifier Design, Simulated Personas, Benchmarking Personal AGI are some of the research areas we work on.\n\n\n\nRESPONSIBILITIES:\n\n - Build Agentic Environments: Design and implement the next generation of \"SimLabs\", ultra-realistic, long-horizon simulation environments where agents learn to navigate ambiguity and maintain context.\n\n - Programmatic and Agentic Verification: Develop rigorous, policy-aware judges and evaluations that measure genuine capability and safety beyond simple benchmarks.\n\n - Close the Loop: Design and execute high-quality post-training runs (CPT, SFT, RL) to deliver frontier performance on open-source models using curated, high-signal data.\n\n - Collaborate: Work daily with the founders and research staff to shape the roadmap and push the state-of-the-art in AI reliability.\n\n - Work on analyzing model failure modes and creating frontier data pipelines which scale with test-time compute.\n\n\n\nABOUT YOU\n\nWe are looking for individuals who demonstrate a rare combination of technical depth, research intuition, and high agency.\n\n - Technical Foundation: A Bachelor’s, Master’s, or PhD in a technical field (CS, Math, Physics, etc.), or a demonstrated \"proof of work\" through significant open-source contributions or industry experience.\n\n - Engineering Rigor: A strong foundation in software engineering with the ability to build robust, scalable infrastructure. You should be comfortable in a Python-friendly, CLI-first development environment.\n\n - ML Fluency: A principled understanding of foundation models, including how they are constructed, evaluated, and optimized.\n\n - Empirical Mindset: Experience conducting research or technical experiments with a focus on reproducibility and data-driven results.\n \n\n\nWHAT WILL MAKE YOU STAND OUT\n\n - Research Taste: You have a strong intuition for identifying what matters in complex problem spaces. You can balance deep research exploration with the pragmatism needed to ship a product.\n\n - Impact-Driven Agency: You care about outcomes, not just activity. You don't wait for a ticket; you identify gaps in the system, build the solution, and ensure it moves real-world metrics for frontier AI labs.\n\n - Domain Expertise: Prior experience with Reinforcement Learning (RLHF/RLAIF), simulation systems, or building long-horizon agentic environments.\n\n - Proven Track Record: A history of contributing to influential ML research (e.g., publications at NeurIPS, ICLR, ICML) or maintaining high-impact open-source projects.\n\n - Post-Training Experience: Experience fine-tuning or evaluating large-scale models to deliver \"frontier performance\" on open-source benchmarks.\n\n\n\n\nWHY JOIN COLLINEAR\n\n - Own the Frontier: Work on the most pressing problem in AI today: making agents reliable enough for production.\n\n - High Density of Talent: Join a small, elite team where you will be pushed to do your life's work.\n\n - Elite Compensation: We offer competitive salary and equity packages to ensure we attract the best of the best.\n\n - Direct Impact: At a seed-backed startup, your work directly shapes the company's trajectory and the future of AI safety.\n\n\n\nCollinear is an equal opportunity employer and values diversity. We do not discriminate on the basis of race, color, religion, sex, gender identity, sexual orientation, national origin, age, disability, veteran status, or any other characteristic protected by applicable law.\n\nThe base salary range for this role in California is $160,000 to $400,000 per year, depending on experience, skills, and qualifications. This role will also be eligible for equity, benefits, and bonuses.\n\nCollinear provides reasonable accommodations for candidates with disabilities throughout the application and hiring process. If you need an accommodation, please contact us.\n\nPursuant to applicable local ordinances, we will consider qualified applicants with arrest and conviction records."},{"id":"4d4af6b1-bfc7-4a28-9d86-5bab73e6e396","title":"MTS - Product","department":"Product","team":"Product","employmentType":"FullTime","location":"Sunnyvale, California","secondaryLocations":[{"location":"San Francisco, CA","address":{"postalAddress":{"postalCode":"94105","addressRegion":"California","streetAddress":"","addressCountry":"United States","addressLocality":"San Francisco"}}}],"publishedAt":"2026-06-24T17:06:56.792+00:00","isListed":true,"isRemote":true,"workplaceType":"Hybrid","address":{"postalAddress":{"addressCountry":"United States"}},"jobUrl":"https://jobs.ashbyhq.com/collinear-ai/4d4af6b1-bfc7-4a28-9d86-5bab73e6e396","applyUrl":"https://jobs.ashbyhq.com/collinear-ai/4d4af6b1-bfc7-4a28-9d86-5bab73e6e396/application","descriptionHtml":"As a Member of Technical Staff - Product, you will help decide what Collinear builds, and then build it. You’ll work directly with researchers and engineers at cutting-edge AI labs, identify their hardest problems, and turn those problems into products that advance how frontier agents are trained and evaluated.
This is a high-ownership, hands-on role at the intersection of Product, Engineering, and Research. You’ll shape Collinear’s product roadmap, prototype solutions, contribute code, run experiments, and work with the team to take ideas from ambiguous customer need to a product used in real research workflows.
Set the product strategy and internal roadmap so the team ships the environments, tasks, and evaluation data that address the hardest open problems at frontier labs
Work directly with researchers and engineers at leading AI labs, becoming deeply familiar with how they build, evaluate, and improve agents
Own important technical product areas such as agent environments, eval and quality systems, etc., from initial discovery through launch and iteration
Turn ambiguous research and customer needs into clear product bets, requirements, prototypes, and measurable outcomes
Drive projects from discovery through delivery. Coordinate implementation across research and engineering teams while contributing directly through prototypes, code changes, tests, technical reviews, and debugging when that is the fastest path forward.
Keep customers and cross-functional teams aligned through clear roadmaps, demos, and playbooks, communicating progress, tradeoffs, risks, and decisions clearly
You have strong product judgment and can create clarity from loosely defined technical problems
You are technically fluent enough to navigate unfamiliar codebases, understand system architecture and data flows, debug failures, review pull requests, and make focused implementation changes
You have worked on developer tools, AI infrastructure, evaluation systems, data platforms, workflow products, or similarly technical products
You can reason carefully about quality: what should be measured, what evidence proves something works
You communicate effectively with researchers, engineers, customers, and company leadership, adjusting the level of technical detail without losing precision
You operate well in a fast-moving environment where the roadmap evolves as new customer and model evidence emerges
You are comfortable being accountable for the outcome even when you are not the sole implementer
You are an unusually effective user of AI development tools and know that generated work still requires strong scoping, review, testing, and judgment
Experience with AI agents, evaluations, benchmarks, reinforcement-learning environments, task generation, or verifier design.
Familiarity with computer-use or MCP-based agent environments.
Experience designing quality or release systems for technically complex customer deliverables.
Experience working directly with frontier AI labs, applied research teams, or enterprise technical customers.
Early-stage startup experience spanning product, engineering, operations, and customer delivery.
We are looking for a talented Member of Technical Staff (MTS - Product) to build scalable features, interactive environments, and user-facing capabilities. In this role, you will bridge the gap between high-performance backends and intuitive user experiences, allowing our customers to seamlessly configure, simulate, and analyze complex workflows. Your primary focus will be on engineering robust, user-centric product features that translate advanced AI/ML capabilities into a seamless, high-performance web application.
End-to-End Quality Ownership: Implement comprehensive automated testing strategies across the entire feature lifecycle—including rigorous unit, integration, and end-to-end (E2E) testing—to guarantee that complex simulation UI and backend states never regress.
Scalable Product Features: Architect, build, and maintain robust, user-facing features using Python (FastAPI) and modern frontend frameworks (React/Next.js) to deliver a seamless end-to-end user experience.
High-Performance Backends: Design and optimize asynchronous backend services capable of handling intensive workloads, coordinating heavy simulation tasks, and managing real-time data streaming.
SimLab Core Workflows: Own the execution and orchestration layer, ensuring that user-configured simulation environments and data pipelines run deterministically, resiliently, and at scale.
Data & State Management: Design and optimize both SQL and NoSQL data layers to manage complex user configurations, log high-volume simulation metrics, and retrieve historical telemetry data efficiently.
API Design & Integration: Build clean, versioned, and intuitive APIs that connect SimLab’s frontend with core AI/ML orchestration engines and external data sources.
Edge-Case Resilience: Proactively architect error-handling mechanisms and defensive code patterns to ensure our products handle unpredictable simulation inputs, high concurrency, and massive datasets without degrading the user experience.
Product Observability: Instrument deep telemetry, logging, and error-tracking across the application stack to monitor feature health in production, quickly isolating and resolving quality bottlenecks before they impact users.
Product Engineering Mindset: Proven experience in a Full-Stack or Product Engineering role, with a passion for building highly interactive, reliable, and user-facing SaaS products (ideally in the developer tool, simulation, or AI space).
Obsession with Reliability: A strong engineering philosophy centered on code correctness and product stability; you don't consider a feature \"done\" until it is fully tested, documented, and resilient against edge cases.
Robust Backend Expertise: Strong software development skills in Python (FastAPI) with a deep understanding of asynchronous programming, concurrent systems, and distributed task queues (e.g., Celery, Redis).
Data Layer Knowledge: Solid understanding of relational and non-relational databases (SQL/NoSQL) and query optimization, particularly for handling large-scale simulation outputs or time-series data.
AI/ML Familiarity: Prior exposure to AI/ML workflows, training pipelines, or NLP/LLM applications. Experience or a strong interest in interfacing products with Reinforcement Learning (RL) or simulation environments is highly desirable.
Engineering Culture: A strong champion of clean code, comprehensive testing (TDD), CI/CD best practices, and building highly maintainable, scalable architectures.
Adaptability: Prior experience in a fast-paced startup or top-tier tech environment where you’ve successfully taken features from ambiguity to production-grade deployment.
Education: Bachelor’s or Master’s degree in Computer Science, Engineering, or a related technical field.
We are looking for a talented Member of Technical Staff (MTS - Engineering) with expertise in React and NextJs (JavaScript frameworks) for frontend development and backend development in Python and Fast API. The ideal candidate will have hands-on experience in DevOps technologies, testing frameworks, database management, and exposure to AI/ML or NLP/LLM projects.
Key responsibilities include:
Develop scalable, responsive web applications using modern frontend frameworks (React/Next.js)
Design and implement high-performance backend solutions using Python and FastAPI, ensuring reliability and scalability
Collaborate with cross-functional teams to define features, enhancements, and deliver product updates
Implement and maintain DevOps best practices for continuous integration and deployment using tools like Jenkins, AWS, Docker, and Kubernetes
Write and maintain comprehensive unit, integration, and end-to-end tests using testing frameworks
Troubleshoot and debug frontend and backend issues, ensuring timely resolution and system optimization.
Work with both SQL and NoSQL databases, optimizing queries for efficient data management
Collaborate with AI/ML teams to build and deploy applications leveraging NLP and LLM technologies
There are a few specific things we’ll be looking for that will help you succeed in this role:
Bachelor’s or Master’s degree in Computer Science/Engineering, or a related field
Experience in full stack development with a focus on both frontend and backend technologies
Proficiency in JavaScript frameworks (React/Next.js) for frontend development
Strong backend development skills in Python (FastAPI) or similar languages
Experience with DevOps tools such as Jenkins, AWS, Docker, and Kubernetes for CI/CD pipelines
Hands-on experience with testing frameworks and a Test-Driven Development (TDD) approach
Expertise in SQL and NoSQL databases, with a solid understanding of database design and optimization
Experience in AI/ML or NLP/LLM projects is highly desirable
Contributions to open-source projects, with an active GitHub portfolio showcasing innovation and expertise, are preferred
Strong problem-solving skills and the ability to work in a fast-paced, collaborative environment
Prior experience in top-tier technology companies or startups is a plus
We are looking for a Member of Technical Staff (MTS - Research) to help us build the data engine for frontier AI. In this role, you will bridge the gap between frontier research and production engineering. You will develop the high-fidelity environments and evaluation stacks that the world’s leading AI labs rely on to stress-test their most advanced agents. Your work will involve iterating on novel RL approaches and translating them into robust, scalable infrastructure that moves the needle on real-world model metrics.
Build Agentic Environments: Design and implement the next generation of \"SimLabs\", ultra-realistic, long-horizon simulation environments where agents learn to navigate ambiguity and maintain context.
Programmatic Verification: Develop rigorous, policy-aware judges and evaluations that measure genuine capability and safety beyond simple benchmarks.
Close the Loop: Design and execute high-quality post-training runs (CPT, SFT, RL) to deliver frontier performance on open-source models using curated, high-signal data.
Rapid Iteration: Debug and iterate across the full ML stack, from infrastructure to model behavior, ensuring our tools remain \"command-line first\" and developer-friendly.
Collaborate: Work daily with the founders and research staff to shape the roadmap and push the state-of-the-art in AI reliability.
We are looking for individuals who demonstrate a rare combination of technical depth, research intuition, and high agency.
Technical Foundation: A Bachelor’s, Master’s, or PhD in a technical field (CS, Math, Physics, etc.), or a demonstrated \"proof of work\" through significant open-source contributions or industry experience.
Engineering Rigor: A strong foundation in software engineering with the ability to build robust, scalable infrastructure. You should be comfortable in a Python-friendly, CLI-first development environment.
ML Fluency: A principled understanding of foundation models, including how they are constructed, evaluated, and optimized.
Empirical Mindset: Experience conducting research or technical experiments with a focus on reproducibility and data-driven results.
Research Taste: You have a strong intuition for identifying what matters in complex problem spaces. You can balance deep research exploration with the pragmatism needed to ship a product.
Impact-Driven Agency: You care about outcomes, not just activity. You don't wait for a ticket; you identify gaps in the system, build the solution, and ensure it moves real-world metrics for frontier AI labs.
Domain Expertise: Prior experience with Reinforcement Learning (RLHF/RLAIF), simulation systems, or building long-horizon agentic environments.
Proven Track Record: A history of contributing to influential ML research (e.g., publications at NeurIPS, ICLR, ICML) or maintaining high-impact open-source projects.
Post-Training Experience: Experience fine-tuning or evaluating large-scale models to deliver \"frontier performance\" on open-source benchmarks.
Collinear.AI is seeking a Member of Technical Staff (Applied Scientist) with deep expertise in engineering sciences to work at the frontier of AI-accelerated simulation. In this role, you will collaborate with customers and internal research teams to build, test, and deploy AI Physics Models.
You will contribute across the full stack: curating high-fidelity simulation datasets, training and evaluating physics-informed models, and delivering production-grade AI solutions directly to engineering teams. Key target domains include computational fluid dynamics (CFD), structural mechanics, semiconductor design, multi-physics modeling, and digital twins.
Working cross-functionally across research, product, and client-facing teams, you will ensure models meet rigorous real-world engineering standards—not just theoretical benchmark metrics.
Execute Simulation Campaigns: Design and orchestrate large-scale simulation campaigns using domain-specific solvers (e.g., OpenFOAM, ANSYS, COMSOL, Abaqus).
Train & Validate Models: Train AI models on physics datasets and conduct rigorous evaluations of coverage, accuracy, and output quality against industrial validation standards.
Build Infrastructure & Tooling: Develop robust automated frameworks for dataset creation, simulation pipeline orchestration, and continuous model evaluation.
Integrate LLMs & Workflows: Architect agentic workflows and Retrieval-Augmented Generation (RAG) systems that seamlessly connect LLMs with engineering simulation pipelines.
Research Collaboration: Partner closely with the research team to analyze training runs, diagnose failure modes, and address data sparsity or architecture bottlenecks.
Technical Project Management: Lead research initiatives and manage technical communications with external engineering teams.
Education: Ph.D. or Master's degree in Machine Learning, Mechanical Engineering, Electrical Engineering, Computational Physics, Structural Mechanics, Semiconductor Engineering, or a related field.
Technical Mastery: Solid grounding in deep learning principles paired with a strong foundation in physics or engineering sciences.
Framework Proficiency: Hands-on experience implementing and training deep learning models.
Software Engineering: Demonstrated ability to write clean, maintainable Python in Linux and High-Performance Computing (HPC) environments.
Communication: Outstanding verbal and written communication skills, with the ability to explain complex technical concepts to both specialized engineers and non-technical stakeholders.
Ownership & Mindset: Self-directed operator who thrives with autonomy, maintains a low-ego approach to collaboration, and excels in fast-paced environments at the intersection of simulation and ML.
Hands-on industrial or academic experience with simulation solvers (e.g., OpenFOAM, ANSYS, COMSOL, Abaqus).
Direct experience applying machine learning to physics simulations or surrogate modeling (e.g., Neural Operators, Physics-Informed Neural Networks).
Track record of automating large-scale simulation workloads on HPC clusters.
Meaningful contributions to large-scale open-source projects or production codebases.
Published research in top-tier machine learning (NeurIPS, ICLR, ICML) or computational engineering conferences/journals.
Strong software engineering discipline, including static typing, unit testing, and CI/CD maintenance.
You will work closely with marketing leadership to execute growth campaigns and experiments across email, outbound, landing pages, audience development, events, community, and marketing operations. Some weeks you may help launch a campaign or improve a funnel. Other weeks you may build a target list, run a dinner, or test a new channel.
This is a hands-on learning role for an early-career marketer who wants to work in the exciting world of AI.
What You’ll Work On
Help plan and execute growth campaigns and experiments, from audience setup and launch through monitoring, follow-up, and reporting.
Build and maintain accurate account, contact, and audience lists for campaigns, events, and outbound efforts.
Set up campaign mechanics such as landing pages, forms, email sequences, tracking links, CRM fields, and simple workflows.
Coordinate campaign timelines and assets with internal contributors, using approved messaging and materials.
Execute promotion and distribution plans across email, outbound, social and relevant community channels.
Plan and run select events and community programs, including invitations, guest lists, venues, vendors, budgets, and on-site execution.
Manage company gifting and swag, including vendors, inventory, recipient lists, packaging, and shipping.
Track campaign and event performance, data quality, costs, conversion, and engagement, then summarize what we learned.
We consistently launch well-run campaigns and experiments without sacrificing quality.
Every campaign and event has a clear audience, operating plan, tracking setup, and follow-up process.
We are seeking an Applied Scientist (Robotics Simulation) to design and build the physics-solving engine behind Collinear’s synthetic data generation platform. In this role, you will architect and optimize a next-generation simulation stack engineered purely for maximum data throughput, procedural environmental scale, and physical fidelity.
You will collaborate closely with GPU optimization engineers, procedural scene generation teams, and AI researchers pushing the limits of model training on synthetic data. The ideal candidate brings deep physics simulation expertise and hands-on experience implementing high-performance solvers on modern parallel GPU architectures.
Physics Solver Innovation: Improve and develop advanced physics solvers and numerical modeling methods for high-degree-of-freedom interactions, complex contact dynamics, deformables, and fluids.
Training Data Optimization: Partner with team members to design simulation environments, domain randomization strategies, and physics parameter ranges that yield high-utility training datasets for downstream AI models.
Procedural Scene & Scale Integration: Collaborate with scene-generation engineers to scale environment variations, physical property distributions, and dynamic multi-agent interactions across billions of simulation steps.
Performance Profiling: Profile and optimize simulation execution at scale, minimizing latency and memory overhead in multi-GPU, parallelized rollout pipelines.
Technical Leadership: Help shape Collinear’s long-term roadmap for high-fidelity physical modeling, differentiable simulation, and synthetic data engine architecture.
Education: MS or PhD in Physics, Computer Science, Applied Math, Engineering, or equivalent hands-on industry experience in physics-based simulation.
Physics Engine Expertise: Strong track record of developing or extending high-fidelity physics engines, rigid-body dynamics solvers, or constraint-based systems. Experience with deformables, fluids, soft bodies, or differentiable simulation is a plus.
Numerical Methods & Dynamics: Deep understanding of classical mechanics, contact modeling, constraint solvers, integrators, and managing accuracy-vs-speed trade-offs for large-scale computation.
GPU Programming: Advanced expertise in CUDA and GPU optimization, with a proven ability to accelerate numerical algorithms on parallel hardware architectures.
Systems Engineering: High proficiency in C++ and Python, with a track record of building reliable, high-throughput software used in production data or ML pipelines.
ML Infrastructure Familiarity: Strong grasp of how ML frameworks consume simulation outputs (e.g., vectorized environments, massively parallel rollouts, synthetic dataset generation).
Fidelity Intuition: Deep intuition for physical realism and dataset distribution drift—understanding how synthetic physical data behaves and how to model environmental variance without relying on physical hardware collection.
Track Record: Publications, open-source contributions, or shipped commercial systems in numerical computing, graphics, physics simulation, or synthetic data generation.
We are extending our environments into biosafety, and we need a domain authority to lead that track.
You will turn the everyday judgment of a biosafety professional into tasks, reference solutions, and graders that measure whether a model reasons about biological risk the way a trained expert would. Because biology is dual-use, you will also help define the boundary: where a model should answer fully, where it should hedge, and where it should decline.
No prior AI experience is required. We will teach you our tooling and workflow. What we cannot teach is the field expertise and the judgment, and that is what we are hiring for.
What you will do
Map real biosafety work into tasks. Risk assessments, biosafety-level assignment and containment decisions, IBC protocol review, exposure and spill response, decontamination and waste streams, shipping of infectious substances, ecotoxicology study design, and environmental release assessments.
Write expert-level prompts and gold-standard answers. Realistic scenarios with the ambiguity a practitioner actually faces, paired with reference solutions that hold up to peer review.
Build rubrics and verifiers. Define what a complete, correct, and safe answer looks like, and what an unsafe or insufficient one looks like. Calibrate your rubrics against other experts so grading is consistent.
Grade model outputs. Score responses for scientific accuracy, regulatory correctness, completeness, and appropriate handling of sensitive information.
Red-team the boundary. Design adversarial and multi-turn prompts that probe where helpful biology becomes operational uplift. Document failure modes clearly enough that engineers can act on them.
Shape the environments. Work with our environment engineers to encode workflow state, tools, and reward signals, and review model trajectories for realism.
Raise the bar for the track. Review tasks written by other contributors and help set the quality standard for biosafety work at Collinear.
Who we are looking for
A PhD in biology, microbiology, virology, immunology, biochemistry, toxicology, ecotoxicology, environmental health, public health, or a closely related field. A masterʼs degree with four or more years of applied biosafety experience also qualifies.
Hands-on laboratory experience with biological agents at BSL2 or above. You have written or reviewed a risk assessment, an SOP, or an IBC submission.
Working knowledge of the frameworks practitioners actually use: the BMBL, NIH Guidelines, OSHA Bloodborne Pathogens standard, Select Agent regulations, DURC and PEPP policy, IATA and DOT rules for Category A and B shipments, and OECD or EPA ecotoxicology test guidelines, as relevant to your specialty.
Sound judgment about dual-use information. You can tell benign research from a credible attempt at harm, and you can explain the difference in writing.
Clear, precise writing. You are comfortable working to a rubric and giving structured, actionable feedback.
Hands-on use of large language models and curiosity about how they fail.
Nice to have
Experience as a biosafety officer, IBC member, or EHS professional. CBSP or RBP certification.
Ecotoxicology or environmental risk assessment practice, including regulatory submissions.
Prior work training, evaluating, or red-teaming AI systems.
Python or basic scripting for working with data and evaluation pipelines.