Mind Robotics is building Physical AI for real-world industrial deployment, starting with the factory floor. We believe the hardest problems in AI are solved when researchers and engineers are hands-on with the physical world every day - and we're looking for people who are passionate about robotics, value ownership, and are excited to tackle difficult problems. Join us if you want to move beyond digital intelligence and put intelligence into motion.
At Mind Robotics, we’re building generalized physical AI—robotic systems capable of dexterous, adaptive, and reasoning-intensive work in real-world industrial environments. Our models sit at the core of this effort, bridging cutting-edge foundation model techniques with real-world robotic execution.
We’re looking for a Research & Modeling Engineer to build and train the core models that power our systems, and ensure they perform reliably on real robots in production environments.
Design and run large-scale training pipelines for multimodal / VLA systems.
Own the full loop: data → training → evaluation → deployment on real robots.
Develop scalable infrastructure for data ingestion, training, and iteration.
Translate model outputs into reliable, high-performance robotic actions.
Work hands-on with robots to debug, iterate, and improve behavior.
Define data strategy (quality, scale, diversity) and evaluation frameworks.
Continuously improve performance across real-world tasks and environments.
Built and trained large-scale models (LLMs, VLMs, or robotics foundation models).
Deep understanding of modern ML, training dynamics, and optimization at scale.
Experience with distributed systems and data pipelines for large-scale training.
Comfortable operating end-to-end: from data → model → real-world deployment (including robots).
Domain strength in at least one: robotics, VLA systems, or training LLMs/VLMs from scratch.
Strong Python proficiency.
Experience with dexterous manipulation or complex robotic tasks.
Experience deploying models in real-world, production environments.
Mind Robotics is building Physical AI for real-world industrial deployment, starting with the factory floor. We believe the hardest problems in AI are solved when researchers and engineers are hands-on with the physical world every day - and we're looking for people who are passionate about robotics, value ownership, and are excited to tackle difficult problems. Join us if you want to move beyond digital intelligence and put intelligence into motion.
At Mind Robotics, we’re building generalized physical AI—robotic systems capable of dexterous, adaptive, and reasoning-intensive work in real-world industrial environments. Our ability to iterate quickly on large-scale models depends on world-class ML infrastructure.
We’re looking for a Research Engineer to build the core systems that enable fast, reliable, and scalable model training—powering everything from experimentation to production deployment.
Design and implement scalable systems for training large ML models.
Enable efficient workflows for data ingestion, training, and iteration.
Develop and optimize distributed training systems across hundreds of GPUs.
Implement strategies for parallelization, sharding, and efficient compute utilization.
Improve training efficiency through techniques such as attention optimizations, kernel fusion, and memory management.
Partner closely with modeling teams to accelerate iteration speed and reduce training costs.
Build internal tools for experiment tracking, monitoring, and debugging.
Implement systems for tracking training performance, failures, and resource utilization.
Debug and resolve bottlenecks across the training stack.
Provide lightweight infrastructure support for deploying and running models in production environments.
Optimize inference performance and reliability where needed.
Support core cloud infrastructure needs for training workloads (without heavy DevOps overhead).
Manage compute resources efficiently across training jobs.
Strong experience building infrastructure for large-scale ML training.
Deep understanding of how modern LLM/VLM systems are trained and scaled.
Proven experience setting up and scaling distributed training across hundreds of GPUs.
Strong understanding of parallelization strategies (data, model, pipeline parallelism).
Strong proficiency in Python programming.
Expert-level proficiency in PyTorch and/or JAX.
Strong understanding of techniques like attention optimization, kernel fusion, and efficient memory usage.
Experience supporting inference systems in production.
Familiarity with robotics or embodied AI workloads.
Experience building tools for experiment management and researcher productivity.
Mind Robotics is building Physical AI for real-world industrial deployment, starting with the factory floor. We believe the hardest problems in AI are solved when researchers and engineers are hands-on with the physical world every day - and we're looking for people who are passionate about robotics, value ownership, and are excited to tackle difficult problems. Join us if you want to move beyond digital intelligence and put intelligence into motion.
Mind Robotics is building robots that learn from real-world experience. That starts with a data engine: the pipelines and infrastructure that turn raw, messy, multimodal sensor streams from robots and human demonstrations into high-quality, well-curated training data at scale.
As a Data Infrastructure Engineer, you'll build and operate the pipelines that turn raw sensor and demonstration data into training-ready datasets — from ingestion off real robots and capture devices, through processing and quality filtering, to the dataloaders that feed model training. The systems work today; your job is to build within the architecture, take ownership of specific pipelines and services, and help harden the system as it scales.
Build and scale ingestion pipelines for high-volume, high-dimensional sensor data.
Build and maintain data quality and curation systems, including both manual review workflows and auto-labeling.
Contribute to storage and retrieval design for large-scale multimodal datasets, including format choices and versioning/lineage.
Build and operate distributed processing infrastructure (batch and streaming).
Debug production issues in live data pipelines — data corruption, schema drift, backpressure, and failures that only show up at scale.
Work with modeling/research partners to understand data quality, format, and structure needs, and translate them into working pipelines.
Participate in design and code review across the data infrastructure stack.
2+ years of software engineering experience, with some exposure to data pipelines, data engineering, or backend systems.
Strong programming fundamentals in Python, with the ability to write performant, production-grade data processing code.
Experience with at least one distributed data processing framework (e.g., Spark, Ray, Dask, or Flink), or strong fundamentals and willingness to ramp up quickly.
Familiarity with data storage concepts — object storage, data lake table formats, and warehouse vs. lake tradeoffs.
Bias for ownership: you've taken features or systems from prototype to production.
Clear communicator who collaborates well with research/modeling partners and more senior teammates.
Experience with workflow orchestration tools (e.g., Airflow, Dagster, Prefect) is a plus.
Experience with streaming ingestion for high-volume, near-real-time data is a plus.
Experience building automated data quality/curation systems (statistical filtering, anomaly detection, deduplication, or using ML models in an annotation/filtering pipeline) is a plus.
Experience with robotics- or embodied-AI-specific data (multi-embodiment datasets, teleoperation/demonstration data, egocentric video) is a plus.
Mind Robotics is building Physical AI for real-world industrial deployment, starting with the factory floor. We believe the hardest problems in AI are solved when researchers and engineers are hands-on with the physical world every day - and we're looking for people who are passionate about robotics, value ownership, and are excited to tackle difficult problems. Join us if you want to move beyond digital intelligence and put intelligence into motion.
At Mind Robotics, we’re building generalized physical AI—robotic systems capable of dexterous, adaptive, and reasoning-intensive work in real-world industrial environments. Delivering this in production requires robust, high-performance robotics software that can reliably operate in complex, real-world settings.
We’re looking for a Robotics Software Engineer to build the runtime systems, middleware, and developer infrastructure that power our robotic platforms—from low-level execution to operator-facing tools.
Design, develop, and maintain runtime systems that enable reliable, low-latency operation of robotic platforms.
Ensure robustness of robotics systems across real-world deployment scenarios.
Implement and optimize robotics middleware for inter-process communication, data serialization, and message passing.
Integrate with frameworks like DDS, Zenoh, or build custom solutions as needed.
Build systems for task scheduling, resource management, and lifecycle management of robotic applications.
Enable scalable and modular execution of complex robotic behaviors.
Ensure that robotics systems meet strict latency and reliability requirements.
Optimize for resource-constrained and embedded environments.
Develop tools to monitor system health, debug failures, and analyze performance.
Design and maintain deployment workflows, CI/CD pipelines, and containerization (e.g., Docker).
Build abstractions, APIs, and tools that enable application engineers to develop robotic behaviors efficiently.
Contribute to operator-facing tools (e.g., UI for monitoring, control, and data annotation workflows).
Focus on developer experience and system ergonomics, ensuring that the platform scales across robots, tasks, and environments.
Strong experience building robotics software systems and middleware.
Experience designing and building runtime systems, execution frameworks, or middleware layers.
Strong understanding of distributed systems and communication patterns in robotics.
Experience working with real-time constraints and performance-critical systems.
Strong proficiency in Python programming.
Familiarity with robotics middleware frameworks (e.g., DDS, Zenoh, ROS2).
Experience with containerization, CI/CD, and codebase management at scale.
Ability to design clean abstractions and APIs for internal users.
Experience building tools or interfaces for operators or engineers.
Background in building operator tools or robotics UIs.
Experience supporting ML-driven robotic systems.
Mind Robotics is building Physical AI for real-world industrial deployment, starting with the factory floor. We believe the hardest problems in AI are solved when researchers and engineers are hands-on with the physical world every day - and we're looking for people who are passionate about robotics, value ownership, and are excited to tackle difficult problems. Join us if you want to move beyond digital intelligence and put intelligence into motion.
Program Management at Mind is an operational catalyst. We own outcomes, drive the trade-offs that keep engineering moving, and set the metabolic rate of the teams we partner with — clearing roadblocks, closing stalled decisions, carrying risk upward, and protecting the focus engineers need to build. As a Software Program Manager you will own the integrated program plan across all of software and build the practice itself: how teams define their own milestones and commit to org-level ones, how we know week over week whether we are on or off track, and how problems surface early enough that we can still do something about them. You will partner directly with hardware program management so that software and hardware roll up into one program picture rather than two competing ones.
Responsibilities:
End-to-End Ownership: Own the software program plan end to end — from roadmap planning through implementation, integration, validation, and release to the fleet — and own the functional outcome, not just the schedule.
Program Practice Design: Establish how software program management works at Mind: the planning rhythm that connects team-level work to org-level milestones, what qualifies as a milestone, what \"done\" means at each one, and the artifacts that support both the individual engineer and leadership in prioritization and decision making.
Milestone Planning: Build software plans that align with org-level milestones, including clear definitions of what “done” means at each step.
Metric Tracking: Monitor and report on key program metrics, and communicate status, blockers, and risks to leadership.
Progress Tracking & Status Integrity: Build the tracking layer that answers whether we are on or off track, drawn from the systems teams already work in.
Risk Mitigation: Identify and escalate technical, dependency, and resourcing risks early, and and drive them to resolution.
Technical / Business Judgment: Engage directly in architecture and design discussions at a depth sufficient to stress-test engineering estimates and pressure-test readiness. Drive decisions at that level with understanding of the impacts to our business goals.
Educational Background: Bachelor's degree in engineering (software / computer science preferred), or equivalent experience.
Industry Experience: 5+ years of experience in technical program management for software products, ideally in robotics, autonomous systems, or another domain where software ships against hardware and real-world deployment constraints.
Proven Track Record: Demonstrated success taking software from early development through release and sustained operation.
Technical Depth: Strong software fundamentals with the ability to engage meaningfully in architecture, planning, and systems integration discussions.
System Complexity: Experience managing programs spanning multiple interdependent software domains along with their dependencies on hardware revisions and the availability of test hardware.
Execution & Ownership: High sense of urgency, comfortable with ambiguity, and driven by an \"engineering curiosity\" to understand how the entire system works.
Mind Robotics is building Physical AI for real-world industrial deployment, starting with the factory floor. We believe the hardest problems in AI are solved when researchers and engineers are hands-on with the physical world every day - and we're looking for people who are passionate about robotics, value ownership, and are excited to tackle difficult problems. Join us if you want to move beyond digital intelligence and put intelligence into motion.
As a Mechanical Design Engineer, you will lead the end-to-end design and development of complex electromechanical hardware and robotic systems. Working in a fast-paced environment, you will rapidly build, test, and iterate on prototypes using 3D printing, CNC machining, and hands-on fabrication. You’ll leverage your deep mechanical intuition to perform core structural, thermal, and tolerance analyses while ensuring designs scale seamlessly through rigorous DFM/DFA practices. Collaborating closely with cross-functional software and electrical teams, machine shops, and contract manufacturers, you will turn ambiguous technical challenges into reliable, high-volume production hardware.
Design and development of mechanical components and assemblies for data collection hardware and robotic devices.
Rapidly build, test, and break prototypes. You’ll use 3D printing, CNC machining, and manual fabrication to validate designs.
Contribute to DFM/DFA efforts to ensure designs can scale from low-volume prototypes to high-volume production.
Source components and work closely with machine shops and CMs to ensure parts are manufactured and shipped to spec and on time.
Perform basic analyses (tolerance, structural, thermal) to validate design decisions
Develop specifications and accelerated test plans to validate the product for its lifetime
Assist in documentation for design reviews and testing.
Collaborate with cross-functional teams (electrical, software, industrial design) to consolidate design requirements and integrate mechanical systems.
Exceptional Mechanical Intuition. You understand how forces, materials, and mechanisms behave instinctively.
5+ years of experience (or equivalent \"hard tech\" projects) building complex electromechanical systems and sensors
Familiarity with rapid prototyping techniques such as 3D Printing, Machining, etc.
High proficiency in SolidWorks, Catia or similar tools
Hands-on problem-solving approach with ability to iterate quickly
Experience designing for volume processes (Injection molding, CNC, etc.) and working with contract manufacturers.
You are comfortable with ambiguity, move fast, and have an \"engineering curiosity\" that drives you to understand how the entire system works, not just your part.
Mind Robotics is building Physical AI for real-world industrial deployment, starting with the factory floor. We believe the hardest problems in AI are solved when researchers and engineers are hands-on with the physical world every day - and we're looking for people who are passionate about robotics, value ownership, and are excited to tackle difficult problems. Join us if you want to move beyond digital intelligence and put intelligence into motion.
We're seeking a hands-on Electrical Design Engineers to design, build, and scale complex electrical systems. In this role, you will own the full design lifecycle—architecting boards, debugging prototypes in the lab, and working directly with manufacturers to bring high-reliability robots to market at scale.
Design and develop complex electrical systems, including power distribution, sensor integration, and motor drive systems.
Execute full-cycle PCB development—schematic capture, component selection, multi-layer layout, and manufacturing file generation (Altium, KiCad, or OrCAD).
Assemble, test, and debug initial prototypes.
Work closely with firmware and mechanical engineers to define connectors, pinouts, and physical constraints.
Develop specifications and accelerated test plans to validate the product for its lifetime.
Interface with PCB fabricators and component suppliers to ensure timely delivery and DFM.
Technical degree in Electrical Engineering or equivalent.
5+ years of experience (or equivalent \"hard tech\" projects) building complex electromechanical systems and sensors.
Deep understanding of DC-DC converters, EMI/EMC mitigation, and standard communication protocols (I2C, SPI, CAN bus, UART).
Experience with hardware validation cycles, debug and measurement methods.
Experience with wire harnesses, cabling & connectors.
Experience designing for volume processes and working with contract manufacturers.
You are comfortable with ambiguity, move fast, and have an \"engineering curiosity\" that drives you to understand how the entire system works, not just your part.
Mind Robotics is building Physical AI for real-world industrial deployment, starting with the factory floor. We believe the hardest problems in AI are solved when researchers and engineers are hands-on with the physical world every day - and we're looking for people who are passionate about robotics, value ownership, and are excited to tackle difficult problems. Join us if you want to move beyond digital intelligence and put intelligence into motion.
We're building robots for industrial deployment that push the limits of what hardware can do and we need someone who's done this before in the trenches, not just observed it from a distance.
You'll be part of building our supply chain from scratch, working shoulder-to-shoulder with our engineering team, and moving at a fast pace. If you've thrived in that environment before - and you're hungry to do it again - this role is for you.
Responsibilities
Identify, evaluate, and onboard suppliers for motors (BLDC, stepper, servo), custom PCBs/PCBAs, injection-molded parts, CNC-machined components, cameras, connectors, and cable assemblies
Own the full procurement cycle — issue quotes, negotiate piece pricing and tooling costs, and execute NDAs, supply agreements, and purchase orders
Build and maintain should-cost models across key commodities to drive cost reduction through competitive bidding, DFM feedback, and supplier consolidation
Source components and work closely with machine shops, CMs, and CEMs to ensure parts are manufactured and shipped to spec and on time
Develop and manage the supply chain readiness plan to move from low-volume prototype builds (10s of units) to initial production runs (100s–1,000s of units)
Track supplier lead times, tooling timelines, and open items across all commodities using program management tools
Manage ongoing supplier performance — partnerships, business reviews, dispute resolution, and corrective action plans
Coordinate inbound logistics from domestic and international suppliers, managing inventory levels to balance carrying cost against schedule risk
Identify single-source dependencies, long-lead-time components, and geopolitical risks; develop mitigation strategies including dual-sourcing and alternate qualification plans
Work hand-in-hand with mechanical, electrical, firmware, and industrial design teams — providing sourcing input on component selection, alternate parts, and make-vs-buy decisions early enough to actually influence outcomes
Qualifications
You've built supply chain at a startup before. You know what it means to operate without a playbook, move fast, and own outcomes end-to-end — not just manage a process someone else designed
Deep sourcing intuition — you understand supplier economics, manufacturing processes, and what drives cost at the component level
5+ years of experience in supply chain, procurement, or sourcing within hardware-intensive industries such as robotics, industrial automation, consumer electronics, aerospace, or automotive
Direct experience sourcing across multiple commodity categories — electromechanical components (motors, actuators), PCBs/PCBAs, injection-molded parts, machined metals, cameras/sensors, or connectors
Familiarity with manufacturing processes such as injection molding, die-casting, MIM, CNC machining, and SMT assembly
Strong negotiation skills with a track record of achieving competitive pricing and favorable terms with both domestic and international suppliers
Experience managing the transition from prototype/NPI builds to volume production, including tooling qualification and first-article inspection processes
Ability to read and interpret engineering drawings, 3D models, and technical specifications well enough to contribute meaningfully in design reviews and supplier technical discussions
Experience working with Chinese, Japanese, or Korean suppliers and contract manufacturers is a strong plus
You thrive in ambiguity, default to urgency, and have the drive to build — not just maintain
Mind Robotics is building Physical AI for real-world industrial deployment, starting with the factory floor. We believe the hardest problems in AI are solved when researchers and engineers are hands-on with the physical world every day - and we're looking for people who are passionate about robotics, value ownership, and are excited to tackle difficult problems. Join us if you want to move beyond digital intelligence and put intelligence into motion.
We're looking for a Senior Safety Engineer who can own functional safety end-to-end for our humanoid platform: hazard analysis, architectural decisions, component selection, certification, and the messy real-world testing in between.
Standards like ISO 3691-4, and ISO 10218 tell you what outcomes you must achieve. ISO 25785-1 is currently under development requiring robot dynamic stability control including zero-energy pose protocols, and fall zone markings. They do not tell you how to get there with a bipedal, dynamically balancing, multi-DOF system that doesn't fit cleanly into the assumptions those standards were written under. We need someone who treats that gap as the interesting part of the job — not a blocker.
Lead the safety lifecycle for our humanoid platform: hazard analysis, risk assessment, SIL/PL determination, safety requirements specification, and validation.
Design safety architectures (E-stops, safety-rated monitored stops, power and force limiting, speed and separation monitoring) appropriate to a mobile, dynamically balancing robot.
Select and justify safety-rated components : controllers, sensors, contactors, and where no off-the-shelf component fits, design or specify alternatives that achieve equivalent safety integrity.
Drive certification efforts , and own the technical file.
Partner with mechanical, electrical, controls, and AI teams to bake safety into the design rather than bolt it on.
Build the test fixtures, procedures, and evidence needed to prove our safety claims.
Stay ahead of evolving standards (ISO 25785-1, the in-progress humanoid-specific work, etc.) and translate them into engineering decisions..
5–10 years of safety engineering experience on industrial robots, collaborative robots, AGVs/AMRs, or comparable machinery.
Deep working knowledge of ISO 13849, IEC 62061, IEC 61508, and ISO 10218 (or ANSI/RIA R15.06); familiarity with ISO/TS 15066 for collaborative applications.
Software safety experience (IEC 61508-3) in addition to systems-level work.
Hands-on experience taking a product through safety certification — not just supporting it, but owning the technical narrative and defending it to assessors.
Comfort with the full toolkit: FMEA, FTA, HAZOP, STPA, and the judgment to know which to reach for.
A creative mindset. Standards rarely dictate components; they dictate outcomes. We want someone who can architect a novel solution, justify it against the standard's intent, and convince a notified body it meets the bar.
Strong communication — you'll be explaining safety tradeoffs to executives, engineers, and external assessors who all need different versions of the same story.
Mind Robotics is building Physical AI for real-world industrial deployment, starting with the factory floor. We believe the hardest problems in AI are solved when researchers and engineers are hands-on with the physical world every day - and we're looking for people who are passionate about robotics, value ownership, and are excited to tackle difficult problems. Join us if you want to move beyond digital intelligence and put intelligence into motion.
We are looking for a hands-on Mechanical Design Engineer to lead the physical design, material selection, and integration of our next-generation tactile sensing systems that allows our robots to feel, manipulate, and interact with the physical world in real time. In this role, you will bridge the gap between hard mechanical architecture and soft, compliant material interfaces, building robust sensory \"skins\" for robotic hands, grippers, and contact-rich interfaces.
Responsibilities
Design and develop mechanical components, assemblies, and skins for tactile sensors integrated into robotic end effectors (fingertips, palms, gripper surfaces) , data collection gloves and other contact-rich subsystems.
Own the tactile sensor stack mechanically from transducer packaging and skin/elastomer design through integration into volume-constrained, cable-routed assemblies.
Develop and tune elastomer / compliant materials (silicone, urethane, gels) including durometer selection, embedded markers or particles, surface texturing, and durability under shear and cyclic loading.
Rapidly build, test, and break prototypes. Use 3D printing and manual fabrication to validate designs.
Perform analyses (FEA, contact mechanics, tolerance, thermal) to validate sensor performance, predict failure modes, and guide design decisions.
Design characterization fixtures and accelerated test plans — indenters, force/torque ground truth setups, shear testers, life-cycle rigs — to validate tactile performance under realistic contact conditions.
Contribute to DFM/DFA efforts so sensor designs scale from low-volume prototypes to high-volume production.
Source components and work closely with machine shops, materials suppliers, and contract manufacturers to ensure parts are manufactured and shipped to spec and on time.
Develop specifications and accelerated test plans to validate the product for its lifetime.
Collaborate cross-functionally with electrical, firmware, controls, and ML teams to translate raw sensor signals into usable manipulation feedback.
Exceptional Mechanical Intuition. You understand how forces, materials, and contact mechanics behave instinctively.
5+ years of experience (or equivalent \"hard tech\" projects) building complex electromechanical systems, sensors, or tactile/haptic devices.
Direct experience with at least one tactile sensing modality — capacitive, resistive, optical/vision-based, magnetic, , piezoelectric, MEMS, or barometric.
Strong materials intuition, particularly with elastomers - hands-on experience with casting, molding, surface treatments, and characterization.
Familiarity with rapid prototyping techniques such as 3D printing, machining, soft lithography, etc.
High proficiency in SolidWorks, Catia, or similar CAD tools.
Hands-on problem-solving approach with ability to iterate quickly.
Experience designing for volume processes (injection molding, casting, CNC, etc.) and working with contract manufacturers.
You are comfortable with ambiguity, move fast, and have an \"engineering curiosity\" that drives you to understand how the entire system works, not just your part.
PhD or research experience in tactile / haptic sensing, soft robotics, or contact-rich manipulation.
Experience designing sensor characterization rigs and analyzing the resulting data.
Working knowledge of sensor electronics and analog front-ends sufficient to collaborate deeply with EE.
Prior work on humanoid hands, dexterous grippers, or prosthetics.
Mind Robotics is building Physical AI for real-world industrial deployment, starting with the factory floor. We believe the hardest problems in AI are solved when researchers and engineers are hands-on with the physical world every day - and we're looking for people who are passionate about robotics, value ownership, and are excited to tackle difficult problems. Join us if you want to move beyond digital intelligence and put intelligence into motion.
As a Thermal Design Engineer at Mind Robotics, you will own the thermal architecture and analysis for our industrial robots, from high-torque actuators and constrained end effectors to dense on-board compute. You will build high-fidelity CFD/FEA models and validate them through hands-on testing, environmental stress, and hardware instrumentation. Working from concept through volume production, you will design scalable cooling solutions like heat sinks, vapor chambers, and TIMs using manufacturing processes like die casting and extrusion. Collaborating closely with electrical, firmware, and controls teams, you will turn complex thermal challenges into high-performance, real-world Physical AI systems.
Own thermal design and analysis across the robot including arms, end effectors, and on-board compute from concept through volume production.
Develop thermal architectures for high-torque-density actuators (motors, gearboxes, drives) in joints, where heat dissipation is the limiting factor on continuous performance.
Design cooling solutions for end effectors within tight volume, mass, and cable-routing constraints.
Develop thermal management for on-board compute (CPUs, GPUs, custom accelerators, power electronics), including airflow, heat sinks, heat pipes, vapor chambers, TIMs, and active cooling where needed.
Build thermal models (analytical, CFD, FEA) and validate them with hands-on testing (thermocouple instrumentation, IR imaging, calorimetry, and accelerated stress tests)
Specify and select thermal components (TIMs, fans, heat pipes, heat sinks, blowers, phase-change materials) and own supplier relationships.
Define thermal limits, derating curves, and protection logic in collaboration with electrical, firmware, and controls teams.
Contribute to DFM/DFA so thermal solutions scale from low-volume prototypes to high-volume production.
Develop accelerated test plans to validate thermal performance over the product's lifetime, including environmental and duty-cycle stress.
5+ years of experience (or equivalent \"hard tech\" projects) designing and validating thermal solutions for complex electromechanical systems.
Strong fundamentals in conduction, convection, and radiation, plus working knowledge of two-phase cooling and heat-pipe / vapor-chamber design.
Hands-on experience with CFD tools (Ansys Icepak, Flotherm, SolidWorks Flow Simulation, or similar) and thermal FEA.
Track record solving thermal problems in at least one of: high-torque-density motors / actuators, dense power electronics, or compute (CPU / GPU / custom accelerator).
Comfortable instrumenting, testing, and debugging thermal performance on real hardware.
Experience designing for volume processes (extrusion, die casting, brazing, stamping) and working with thermal component suppliers and contract manufacturers.
High proficiency in SolidWorks, Catia, or similar CAD tools.
Hands-on problem-solving approach with the ability to iterate quickly.
You are comfortable with ambiguity, move fast, and have an \"engineering curiosity\" that drives you to understand how the entire system works, not just your part.
Experience in robotics, prosthetics, EV powertrains, drones, or other dense electromechanical systems.
Familiarity with motor / actuator thermal modeling (winding hot-spot estimation, lumped-parameter networks).
Experience with liquid cooling or hybrid air / liquid systems.
PhD or research experience in thermal sciences.
Mind Robotics is building Physical AI for real-world industrial deployment, starting with the factory floor. We believe the hardest problems in AI are solved when researchers and engineers are hands-on with the physical world every day - and we're looking for people who are passionate about robotics, value ownership, and are excited to tackle difficult problems. Join us if you want to move beyond digital intelligence and put intelligence into motion.
At Mind Robotics, we're building generalized physical AI—robotic systems capable of dexterous, adaptive, and reasoning-intensive work in real-world industrial environments. Firmware is the connective tissue between intelligence and action; the layer where commands become motion.
We're looking for a Firmware Engineer to own the embedded software that brings our robots to life — from low-level motor control and real-time scheduling to the communication buses that tie the whole system together.
Write, test, and maintain production-quality firmware in C/C++ or Rust for microcontrollers and embedded processors across the robot.
Implement and debug embedded communication protocols — CAN, EtherCAT, I2C, SPI, UART — across distributed hardware nodes.
Configure and manage microcontroller peripherals — GPIO, ADC, PWM, timers, and DMA — to interface with sensors, actuators, and power electronics.
Integrate sensors (encoders, IMUs, torque sensors, force/torque arrays) into the robot's real-time pipeline.
Work closely with electrical engineers on board bring-up, pinout definitions, and hardware-software interface design.
Develop and tune real-time control loops for motors, actuators, and other physical systems under hard timing constraints.
Build tooling to support firmware validation, regression testing, and hardware-in-the-loop systems.
Triage and debug complex interactions between firmware, hardware, and higher-level software during development and field deployment.
Technical degree in Electrical Engineering, Computer Engineering, or equivalent experience.
Demonstrated experience (through work, research, or projects) building firmware for real physical systems — robots, motors, power electronics, or similar.
Deep fluency in C/C++ and/or Rust for embedded targets; comfort reading datasheets and writing drivers.
Hands-on experience with RTOS environments (FreeRTOS, Zephyr, or similar) — task scheduling, priority management, and real-time guarantees.
Strong working knowledge of embedded communication protocols: CAN, EtherCAT, I2C, SPI, UART.
Direct experience configuring microcontroller peripherals (GPIO, ADC, PWM, timers, DMA) and understanding their impact on system timing and performance.
Experience integrating sensors and actuators at the firmware level — not just using libraries, but understanding the hardware underneath.
Proven ability to debug across the hardware/software boundary with oscilloscopes, logic analyzers, and JTAG.
You are comfortable with ambiguity, move fast, and have an \"engineering curiosity\" that drives you to understand how the entire system works, not just your part.
Experience with safety-critical or deterministic real-time systems (IEC 61508, ISO 26262, or similar).
Familiarity with field-oriented control (FOC) or other motor control algorithms.
Experience with Linux-based embedded systems and the boundary between RTOS and application layers.
Python scripting for test automation, data logging, or hardware validation.
Mind Robotics is building Physical AI for real-world industrial deployment, starting with the factory floor. We believe the hardest problems in AI are solved when researchers and engineers are hands-on with the physical world every day - and we're looking for people who are passionate about robotics, value ownership, and are excited to tackle difficult problems. Join us if you want to move beyond digital intelligence and put intelligence into motion.
At Mind Robotics, we're building generalized physical AI — robotic systems capable of dexterous, adaptive, and reasoning-intensive work in real-world industrial environments. Controls is where intelligence becomes motion: the layer that translates high-level commands into precise, safe, and coordinated physical behavior across every joint and actuator on the robot.
We're looking for a Controls Engineer to own the algorithms and software that make our robots move well — from low-level actuator loop design to full-body motion control and the simulation infrastructure that keeps us iterating fast.
Design, implement, and tune control algorithms — PID, state-space, model-based, and beyond — for joints, actuators, and whole-body robot motion.
Develop and maintain high-fidelity dynamic models of robot subsystems to support simulation, analysis, and controller design.
Analyze real-world robot data to assess controller performance, identify regressions, and drive targeted improvements.
Work closely with firmware engineers to implement control algorithms under hard real-time constraints in C/C++ or Rust.
Collaborate with mechanical and electrical engineers to characterize hardware, close the loop between physical design and software performance, and define actuation requirements.
Define and execute test plans that validate control system performance across the full operating envelope of the robot.
M.S. or Ph.D. in controls, robotics, or a related field, or equivalent hands-on experience building and deploying control systems for real physical hardware.
Demonstrated experience (through work, research, or projects) designing and deploying control systems for real physical systems — robots, actuators, drones, or similar.
Strong foundation in control theory: classical (PID, lead-lag), modern (state-space, LQR/MPC), and familiarity with nonlinear systems.
Experience building dynamic models and using simulation tools (MATLAB/Simulink, Python, Julia, or equivalent) to inform and validate controller design.
Hands-on experience tuning controllers on real hardware and debugging unexpected behavior with real data.
Proficiency in C/C++ or Rust for real-time control implementation.
You are comfortable with ambiguity, move fast, and have an \"engineering curiosity\" that drives you to understand how the entire system works, not just your part.
Nice to Have
Experience with whole-body control, trajectory optimization, or model predictive control on legged or manipulator systems.
Familiarity with field-oriented control (FOC) or other motor control algorithms at the firmware level.
Exposure to functional safety standards (ISO 26262, IEC 61508, or similar).
Experience with ROS2 or similar robotics middleware.
Mind Robotics is building Physical AI for real-world industrial deployment, starting with the factory floor. We believe the hardest problems in AI are solved when researchers and engineers are hands-on with the physical world every day - and we're looking for people who are passionate about robotics, value ownership, and are excited to tackle difficult problems. Join us if you want to move beyond digital intelligence and put intelligence into motion.
We’re hiring a Forward Deployed Engineer to be our hands and eyes onsite at a key customer in Normal, IL. You’ll be responsible for our fleet of data collection devices — diagnosing and repairing devices, provisioning new hardware, troubleshooting networking, and running the diagnostic workflows that keep the system healthy. This role will start with critical robot data collection devices and then transition into robot deployment support.
This is a forward-deployed role with strong support from HQ. You won’t be alone: our engineering team is one Slack message away, and you’ll work closely with them to escalate complex issues, ship fixes back into the product, and bring the voice of the customer into our roadmap. The ideal candidate is technically curious, calm under pressure, and energized by being the person on the ground who makes things work.
Responsibilities
Provision new hardware by installing and configuring Linux, deploying our software stack, and validating device health before bringing units into production.
Run diagnostic scripts, collect and interpret system logs, and execute command-line workflows to identify root causes of device or network issues.
Configure and troubleshoot local networking (IP, DHCP, DNS, VLANs, Wi-Fi/Ethernet) to ensure reliable device connectivity to our backend.
Serve as the customer's primary technical point of contact onsite, coordinating closely with HQ engineering and support to resolve issues and feed field learnings back into the product.
Develop code fixes for issues found in the field, including root-causing problems down to the code level and submitting changes through Git and code review.
Test and validate SW trials on field devices by running test plans, capturing results and regressions, and giving a go/no-go before builds roll out fleet-wide.
Be part of the SW sprints by joining planning, standups, and retros, picking up field-related tickets, and helping prioritize the backlog based on customer impact.
Bridge the gap between the SW team and FDE by turning field observations into reproducible bug reports (logs, steps, telemetry) and relaying software changes and new workflows back to the onsite team.
Build scripts and small tools that automate provisioning, diagnostics, log collection, and routine fleet maintenance.
Monitor fleet health using dashboards, metrics, and logs to catch issues early and verify that deployed fixes resolved the problem.
Support staged software rollouts and rollbacks across device groups.
Maintain runbooks, troubleshooting guides, and known-issue documentation.
Qualifications
Hands-on hardware and software troubleshooting experience, ideally with microcomputers, embedded devices, cameras, or similar single-board computers.
Comfort with Linux system administration: installing distributions, package management, systemd services, file permissions, and basic shell navigation.
Working knowledge of Bash (and ideally Python) for reading and running simple diagnostic scripts and automating routine tasks.
Solid networking fundamentals: TCP/IP, subnets, DNS, DHCP, basic firewall rules, and fluency with tools like ping, traceroute, tcpdump, and ssh.
Strong customer-facing communication skills and the self-direction to operate independently onsite while staying tightly coordinated with a remote HQ team.
Software development experience, with the ability to read, debug, and write production code (Python preferred).
Experience with Git-based workflows (branching, pull requests, code review) and working in an Agile/sprint environment.
Experience with software testing and validation: writing test cases, reproducing bugs, and clearly documenting results.
Prior experience in a field service, forward deployed, or solutions engineering role (is a plus).
Familiarity with DevOps and software infrastructure tools such as Docker, Ansible, Portainer, etc (is a plus).
Familiarity with computer vision systems, IP cameras, or industrial IoT deployments (is a plus).
Experience with Git, basic scripting in Python, or remote device management tools (is a plus).
End-to-End Ownership: Lead the hardware development schedule from initial architecture and prototyping through validation, industrialization, and production ramp.
Program Planning: Create and maintain comprehensive program plans, Gantt charts, and action trackers across all active hardware subsystems (actuators, electronics, structures, and sensors).
Metric Tracking: Monitor and report on key program metrics—including cost, mass, reliability, and performance—against the schedule, while communicating status, blockers, and risks to leadership.
Risk Mitigation: Identify and escalate technical and supply chain risks early, leading rapid reaction plans to keep programs on track.
Cross-Functional Alignment: Coordinate across mechanical, electrical, firmware, and software teams to align design milestones, integration timelines, and test readiness.
Supply Chain Collaboration: Partner with supply chain, supplier industrialization, and contract manufacturers to ensure parts and assemblies are delivered on time and to specification.
Configuration Management: Drive rigorous design reviews, change control processes, and BOM accuracy across all subsystems to ensure documentation is efficient and precise.
Validation & Testing: Support the planning and execution of Design Validation Testing (DVT) and Production Validation Testing (PVT) across all hardware subsystems.
Educational Background: Bachelor’s degree in engineering (mechanical, electrical, or a related field) or equivalent practical experience.
Industry Experience: 5+ years of experience in technical program management for complex hardware products (e.g., robotics, consumer electronics, automotive, or aerospace).
Proven Track Record: Demonstrated success in shipping physical products from initial concept through mass production.
Technical Depth: Strong technical fundamentals with the ability to engage meaningfully in mechanical, electrical, and firmware design discussions without requiring translation.
System Complexity: Experience managing programs involving electromechanical systems with multiple interdependent subsystems (motors, PCBAs, sensors, structural assemblies).
NPI Mastery: Familiarity with New Product Introduction (NPI) processes, including tooling qualification, PPAP, DVT/PVT, and first-article inspections.
Manufacturing Engagement: Experience working directly with contract manufacturers and global suppliers on industrialization and production readiness.
Execution & Ownership: High sense of urgency, comfortable with ambiguity, and driven by an \"engineering curiosity\" to understand how the entire system works.
Mind Robotics is building Physical AI for real-world industrial deployment, starting with the factory floor. We believe the hardest problems in AI are solved when researchers and engineers are hands-on with the physical world every day - and we're looking for people who are passionate about robotics, value ownership, and are excited to tackle difficult problems. Join us if you want to move beyond digital intelligence and put intelligence into motion.
A a Software Engineer on Edge & Field Systems, you'll be one of the founding engineers on the team that owns our application layer, reporting to our Head of Application Engineering. The systems work today; your job is to help harden and productionize them for scale — from 10 capture stations at one site to hundreds across multiple OEM plants, and from a handful of teleop stations to a multi-shift fleet. This is early, hands-on, 0-to-1 engineering: you'll ship code that runs on real hardware in real factories, and see its effect on robot behavior.
Build the field capture stack — edge software for our data collection rigs: device management, sensor orchestration, and real-time data quality monitoring that holds up on a factory floor, with workflows optimized for I/O-, compute-, and network-bound constraints
Ship the teleop stack — collection, evaluation, and operator tooling for our teleoperation stations, including metrics, task management, and the workflows our robot operators use every day
Build and contribute to integration and validation testing for field software, including hardware-in-the-loop (HIL) test setups that catch failures before they reach a live site
Build monitoring and dashboards that give visibility into fleet health, capture quality, and station uptime across sites, accounting for intermittent connectivity and bounded bandwidth in the field
Make deployment repeatable — build tooling and configuration systems that make onboarding a new manufacturing site a config change, not a custom engineering project, including device provisioning workflows
Support the field — debug issues on live systems, instrument for observability, and work directly with site operations staff and robot operators who depend on your software
2+ years of software engineering experience building production systems
Strong programming fundamentals and comfort working across the stack — edge devices, services, and local data pipelines
Experience with at least one of: edge computing, device or fleet management, real-time systems, robotics or sensor systems
Bias for ownership: you've taken features or systems from prototype to production and supported them in the field
Clear communication and close collaboration with product, research, and operations partners
Hands-on experience with sensor data (video, depth, IMU, force/torque) and the infrastructure to collect and monitor it in real time is a plus
Experience with hardware-in-the-loop or simulation-based testing (e.g., Isaac Sim, Gazebo, MuJoCo) is a plus
Background in robotics, autonomous vehicles, industrial IoT, or teleoperation systems is a plus
Exposure to manufacturing or other industrial environments is a plus
Mind Robotics is building Physical AI for real-world industrial deployment, starting with the factory floor. We believe the hardest problems in AI are solved when researchers and engineers are hands-on with the physical world every day - and we're looking for people who are passionate about robotics, value ownership, and are excited to tackle difficult problems. Join us if you want to move beyond digital intelligence and put intelligence into motion.
We are looking for our Electrical Engineering Lead to own the end-to-end electrical architecture for our advanced robotic systems. This is a hands-on, deeply technical leadership role for an engineer who leads from the bench, personally driving schematic design, PCB layout, and low-level fault isolation, rather than managing from a distance.
As a technical authority and mentor, you will elevate the team’s standard for engineering excellence while making critical trade-offs across power electronics, motor drives, high-speed sensing stacks, and EtherCAT-based distributed actuation.
Lead, mentor, and scale the EE team (power electronics, sensing, and electrical design) while raising the technical bar through hiring and onboarding.
Own the robot’s end-to-end electrical architecture (power, actuation, sensing, compute integration, and harnessing) and overall electrical budget.
Set EE design standards, review schematics/layouts, and arbitrate trade-offs across signal integrity, EMI/EMC, thermals, and cost.
Partner with mechanical, firmware, and perception teams to translate product requirements into hardware specs and integrate high-speed sensor stacks.
Lead system-level electrical bring-up, fault isolation, and root-cause debugging across board boundaries from the lab bench.
Establish lightweight, scalable workflows for design reviews, schematic/layout sign-offs, ECO discipline, and roadmapping.
Drive DFM/DFA, collaborate with PCB fabricators and contract manufacturers, and secure second-source components for production ramp.
Technical degree in EE (or equivalent) with 8+ years of full-cycle board development (schematic to production) for complex robotic, automotive, or electromechanical systems.
A first-principles engineer who actively designs, lays out, and debugs hardware rather than managing purely from a distance.
Deep, hands-on experience with EtherCAT (topology, distributed-clock sync, PDO mapping) for distributed actuation and sensing.
Mastery of at least one core domain—power electronics/motor drives or sensor/mixed-signal front-ends—with broad fluency across both.
Strong grasp of high-current power architectures, rail partitioning, budgeting, and circuit protection (OVP/UVP/OCP/OTP).
Solid command of EMI/EMC, grounding, and ESD mitigation in noisy, motor-dense environments.
Fluency with high-speed serial interfaces and protocols (SPI, I2C, UART, PMBus), plus enough firmware literacy to debug hardware boundaries.
Proven experience mentoring engineers, managing risk, and driving multi-board hardware projects through volume manufacturing.
Mind Robotics is building Physical AI for real-world industrial deployment, starting with the factory floor. We believe the hardest problems in AI are solved when researchers and engineers are hands-on with the physical world every day - and we're looking for people who are passionate about robotics, value ownership, and are excited to tackle difficult problems. Join us if you want to move beyond digital intelligence and put intelligence into motion.
At Mind Robotics, we're building generalized physical AI — robotic systems capable of dexterous, adaptive, and reasoning-intensive work in real-world industrial environments. Validation is how we make sure our robots actually work — not just once on a bench, but every time, on every robot, in the field. Closing the gap between simulation, hardware test, and the real robot is what makes that reliability possible.
We're looking for a Software Engineer - Validation & Simulation to build the test infrastructure, integrate simulation, and develop validation pipelines that let our firmware, controls, and robot runtime teams move fast without breaking robots.
Responsibilities:
Design and build automated test suites that validate firmware, controls, and runtime behavior across software-in-the-loop (SIL) environments.
Integrate simulation environment inputs and dynamic models that stand in for the real robot — including simulated sensor inputs like camera feeds — so runtime and middleware teams can develop without physical hardware.
Own regression test infrastructure and tooling for result analysis, failure triage, and coverage tracking across firmware, controls, runtime, and hardware.
Collaborate with firmware, controls, and runtime engineers to define testability requirements and turn real-world failures into repeatable automated test cases.
Drive validation burn-down during integration and assembly milestones, tracking issues to closure across teams.
Technical degree in Computer Science, Computer Engineering, Electrical Engineering, or equivalent experience and evidence of exceptional ability.
Strong software engineering fundamentals, with fluency in Python and comfort in C/C++ or Rust for debugging at the firmware level.
Demonstrated experience building validation, simulation, or test infrastructure for real physical systems — robots, motors, actuators, or similar.
You are comfortable with ambiguity, move fast, and have an \"engineering curiosity\" that drives you to understand how the entire system works, not just your part.
Experience validating or testing robotics middleware or runtime systems (ROS2 or similar).
Experience with dynamic modeling or simulation tools (MATLAB/Simulink, MuJoCo, Gazebo, or equivalent) for robotic or actuated systems.
Familiarity with functional safety standards (IEC 61508, ISO 13849, ISO 26262, or similar) and safety validation processes.
Comfort with lab equipment (oscilloscopes, power supplies, logic analyzers) and embedded debugging tools (JTAG, SWD).
Experience with agent-driven coding practices (e.g., Claude Code, Codex, Cursor) to accelerate test and software development.
Mind Robotics is building Physical AI for real-world industrial deployment, starting with the factory floor. We believe the hardest problems in AI are solved when researchers and engineers are hands-on with the physical world every day - and we're looking for people who are passionate about robotics, value ownership, and are excited to tackle difficult problems. Join us if you want to move beyond digital intelligence and put intelligence into motion.
Mind Robotics is building Physical AI for real-world industrial deployment, starting with the factory floor. We believe the hardest problems in AI are solved when researchers and engineers are hands-on with the physical world every day - and we're looking for people who are passionate about robotics, value ownership, and are excited to tackle difficult problems. Join us if you want to move beyond digital intelligence and put intelligence into motion.
The Role
Our data collection operation at the Rivian manufacturing facility in Normal, IL runs on the work of our Field Technicians. You are the reason our cameras are online, our docking stations are healthy, and our data pipeline stays intact across every shift. When something breaks, you're the first response.
This is a hands-on role in a live industrial environment. You won't be sitting at a desk. You'll be walking the floor, working with your hands, troubleshooting real problems in real time, and logging what you find so the engineering team can act on it. The work is physical, detail-oriented, and important — our AI team depends on clean, continuous data, and that starts with you.
Responsibilities:
Station setup and maintenance. Help set up, wire, and configure data capture stations across the factory floor. Maintain the physical health of your area — cameras, docking stations, cabling, and charging systems — so every shift starts clean and every piece of hardware comes back in good shape.
Hands-on troubleshooting. When something isn't working, you diagnose it, resolve what you can, and escalate to your supervisor with the right information when you can't. You keep downtime short and document what happened so we don't see the same issue twice.
Light assembly (< 50 units) and repair. Perform basic assembly, hardware repair, and equipment upkeep as needed. You're comfortable working with consumer electronics and comfortable learning new hardware quickly.
Process observation and feedback. You're on the floor every shift, which means you see things engineers working remotely don't. We want your observations — friction points, recurring issues, ideas for how stations or workflows could be improved — logged and reported consistently.
On-floor communication. When someone at the factory has questions about what we're doing or why, you're often the one answering. You explain it clearly and represent the team well.
Someone who takes ownership of their tasks without being asked. You notice when something is off and you do something about it.
Comfortable with consumer electronics — cameras, charging systems, basic cabling. You don't need to be an engineer, but you need to be someone who figures things out.
Physically able to walk an active factory floor throughout a full shift. This is a standing, moving role in an industrial environment.
A clear, timely communicator. You escalate problems fast, log them accurately, and give your supervisor the context they need to act. You don't sit on an issue.
Friendly and approachable. You're comfortable talking to people you don't know, and you can explain technical or unfamiliar work in a way that puts people at ease.
Reliable and consistent. Our operation runs on shifts, and the team depends on you showing up prepared and ready.
Comfortable in a fast-moving, sometimes ambiguous environment. Things change on a factory floor. We want someone who adapts without losing their attention to detail.
Mind Robotics is building Physical AI for real-world industrial deployment, starting with the factory floor. We believe the hardest problems in AI are solved when researchers and engineers are hands-on with the physical world every day - and we're looking for people who are passionate about robotics, value ownership, and are excited to tackle difficult problems. Join us if you want to move beyond digital intelligence and put intelligence into motion.
At Mind Robotics, we're building generalized physical AI—robotic systems capable of dexterous, adaptive, and reasoning-intensive work in real-world industrial environments.
Our robots rely on a sophisticated software platform that spans cloud infrastructure, robotics middleware, simulation, machine learning, and deployment to physical hardware. We're looking for a DevOps Engineer to build and operate the infrastructure that enables engineers to develop, test, deploy, and monitor robotic systems reliably at scale.
You'll work across software, ML, and robotics teams to automate infrastructure, streamline deployments, improve developer productivity, and ensure our robots can be continuously updated and monitored both in the lab and in production.
Responsibilities:
Design, deploy, and maintain scalable cloud infrastructure using AWS, GCP, or Azure.
Build and maintain Infrastructure as Code using Terraform or similar tools.
Develop CI/CD pipelines for robotics software, embedded applications, and machine learning workflows.
Automate software deployment across development robots, test environments, and production fleets.
Improve build systems, artifact management, release processes, and version control workflows.
Manage Kubernetes clusters and containerized services supporting robotics applications.
Monitor infrastructure health, application performance, and production systems using modern observability tools.
Improve system reliability, uptime, security, and disaster recovery processes.
Build internal developer tooling that increases engineering productivity.
Partner with robotics, firmware, and ML teams to create reproducible development environments.
Support simulation infrastructure and large-scale testing pipelines.
Optimize cloud resource utilization and infrastructure costs.
Implement security best practices for infrastructure, secrets management, networking, and access controls.
Participate in incident response, root cause analysis, and continuous operational improvements.
4+ years of experience in DevOps, Platform Engineering, Site Reliability Engineering, or Infrastructure Engineering.
Strong experience administering Linux systems.
Experience with cloud platforms such as AWS, GCP, or Azure.
Experience with Kubernetes and Docker in production environments.
Strong Infrastructure as Code experience (Terraform preferred).
Experience building CI/CD pipelines using GitHub Actions, GitLab CI, Jenkins, Buildkite, or similar platforms.
Experience with configuration management and automation tools.
Proficiency with Python, Go, or Bash for infrastructure automation.
Experience implementing monitoring and logging solutions (Prometheus, Grafana, Datadog, ELK, OpenTelemetry, etc.).
Strong understanding of networking, security, authentication, and distributed systems.
Experience troubleshooting production systems under operational constraints.
Experience supporting robotics, autonomous systems, or embedded software teams.
Experience deploying software to fleets of physical devices.
Experience supporting GPU infrastructure and ML training clusters.
Experience with NVIDIA GPUs, CUDA environments, or distributed training systems.
Experience managing artifact repositories and large binary assets.
Experience supporting simulation environments.
Knowledge of software supply chain security and SBOMs.
Experience with edge computing or IoT deployments.
Mind Robotics is building Physical AI for real-world industrial deployment, starting with the factory floor. We believe the hardest problems in AI are solved when researchers and engineers are hands-on with the physical world every day - and we're looking for people who are passionate about robotics, value ownership, and are excited to tackle difficult problems. Join us if you want to move beyond digital intelligence and put intelligence into motion.
At Mind Robotics, we're building generalized physical AI — robotic systems capable of dexterous, adaptive, and reasoning-intensive work in real-world industrial environments. Systems engineering is the discipline that holds it all together: translating mission requirements into hardware reality and ensuring every subsystem behaves as a coherent whole.
We're looking for a Systems Engineer to own integration across the robot — from decomposing high-level behaviors into measurable specs, to debugging the hard interactions that only surface when everything is running together. You'll collaborate with hardware and firmware teams on system architecture, and define and own the specs for how the robot behaves. You'll be on the bench as much as you're in a doc, sitting at the intersection of mechanical, electrical, firmware, and controls, accountable for the system from concept through field deployment.
Contribute to architectural decisions for new robot subsystems — communication protocols, control strategies, and hardware-software partitioning — alongside firmware/controls, mechanical, and electrical teams
Collaborate with electrical, mechanical, safety, and firmware teams to define interfaces and validation strategies early, and lead calibration and tuning efforts to hit performance targets
Define software requirements from high-level system requirements, using first-principles analysis and hardware/model-based testing — from top-level robot performance targets down to subsystem budgets and interfaces
Prototype and characterize components and full systems; design early builds to de-risk new technologies before committing to production architecture
Debug hardware-software interactions hands-on during design, launch, and field deployment — pulling and analyzing logs, adding debug instrumentation/tracing to firmware and software, and recompiling and re-flashing to isolate root cause — and surface nth-order impacts of electrical, software, or process changes before they become problems
Analyze failure modes, complete DFMEAs/HARAs, and define system-level test plans from bench testing through field deployment
Drive readiness reviews, sign-off, and post-launch monitoring across firmware, hardware, controls, reliability, and manufacturing stakeholders
Degree in Computer Engineering, Electrical Engineering, Mechanical Engineering, or equivalent experience
2+ years in systems engineering or a cross-disciplinary hardware role involving complex electromechanical or robotic products
Strong hardware and software fundamentals — able to bridge electrical schematics, embedded communication protocols, and firmware architecture; comfortable with a scope and multimeter during bring-up
Hands-on experience debugging complex subsystems involving networked microprocessors and software-controlled electromechanical devices — comfortable reading, modifying, and recompiling C/C++ firmware/software yourself to add debug logging, instrument code paths, and iterate on a fix, not just reading code to hand off to firmware engineers
Experience defining requirements from high-level system behaviors and making them measurable and testable; able to write scripts or tooling to improve bring-up and validation efficiency
Proficient in data analysis and visualization — able to instrument systems, extract signal from noisy hardware data, and communicate findings clearly across mechanical, electrical, and software teams
You are comfortable with ambiguity, move fast, and have an \"engineering curiosity\" that drives you to understand how the entire system works, not just your part
Background in robotics, EV powertrains, drones, or other dense electromechanical systems
Exposure to manufacturing self-test design or service diagnostics
Experience with functional safety standards (ISO 26262, IEC 61508) or automotive/robotics validation processes
Experience in failure modes analysis, HARA, and FTA
Mind Robotics is building Physical AI for real-world industrial deployment, starting with the factory floor. We believe the hardest problems in AI are solved when researchers and engineers are hands-on with the physical world every day - and we're looking for people who are passionate about robotics, value ownership, and are excited to tackle difficult problems. Join us if you want to move beyond digital intelligence and put intelligence into motion.
At Mind Robotics, we're building generalized physical AI — robotic systems capable of dexterous, adaptive, and reasoning-intensive work in real-world industrial environments. Deploying robots in real factories, reliably and safely, depends on the infrastructure that ships software to them, validates it before it goes live, and tells us when something's wrong once it's out there.
We're looking for a Tech Lead to own fleet deployment, testing infrastructure, and observability for our field-deployed robots. This is an IC-heavy role: you're building and hardening these systems yourself, not just defining processes for others.
This is a role for someone who wants to own the hardest problems in getting robotics software safely and reliably into production at scale, and ship them personally.
Architect fleet-wide deployment: OTA updates, versioned rollouts, staged/canary release, rollback strategies, and configuration management across heterogeneous robot hardware in the field
Design and build testing infrastructure — hardware-in-the-loop (HIL) test reporting, simulation-based regression testing, and pre-deployment validation gates that catch failures before they reach a live site
Build and evolve the fleet management platform — the tooling, dashboards, and APIs used to track robot inventory, software/config versions, health state, and deployment status across every unit and site
Own CI/CD pipelines that gate deployment on more than unit tests, and the full path from build artifact to a running process on a physical robot at a remote site
Set technical standards and do deep design/code review across deployment, testing, and dev ops
Work directly with engineering and research teams to build tools and systems that increase team velocity
Design logging infrastructure & visualization tooling
Strong software engineering in Python and/or Rust/C++, with experience building tooling and services
Experience with fleet/device management systems: OTA update mechanisms, versioning schemes, staged rollout and rollback design, and remote configuration management across many physical devices
Hands-on experience with integration and systems testing for hardware-attached software — hardware-in-the-loop testing, simulation-based regression suites (Isaac Sim, Gazebo, MuJoCo, or similar), and CI/CD pipelines that gate on more than unit tests
Observability engineering for edge/field deployments: telemetry pipelines, structured logging, and alerting
Containerization and edge orchestration (Docker, and edge-appropriate orchestration/agent frameworks) for packaging and deploying software onto heterogeneous robot compute
Track record of taking deployment/testing systems from ad hoc to reliable, repeatable process as the number of deployed units and sites grows
Exposure to functional safety concepts relevant to physical systems
Background in electric vehicle OTA, industrial IoT, or other domains with similar field-reliability and fleet-management constraints
Experience scaling deployment/testing infrastructure
Mind Robotics is building Physical AI for real-world industrial deployment, starting with the factory floor. We believe the hardest problems in AI are solved when researchers and engineers are hands-on with the physical world every day - and we're looking for people who are passionate about robotics, value ownership, and are excited to tackle difficult problems.
Own the mechanical design of A-surface plastic components for the robot — covers, skins, shrouds, bezels, and cosmetic enclosures — from industrial design intent through production tooling.
Develop Class-A surfaces and translate ID surfacing into manufacturable parts, holding gap-and-flush, parting line, draft, and aesthetic intent across the full assembly.
Select polymers and processes across the cosmetic stack: ABS, PC, PC/ABS, PA and glass-filled grades, TPU/TPE, and specialty resins — balancing appearance, impact and creep behavior, chemical and UV resistance, flammability, and cost.
Define and own GD&T, tolerance stack-ups, and datum schemes across multi-part plastic assemblies and their interfaces to structures, actuators, and electronics.
Drive DFM and DFA for injection molding, insert molding, overmolding, and secondary operations — gate and ejector strategy, wall thickness, ribbing and boss design, warp and sink mitigation, and shutoff design.
Own cosmetic specification: textures, gloss levels, color matching, painting, plating, laser etching, and in-mold decoration, plus the defect standards and limit samples that go with them.
Work directly with tool shops, molders, and CMs through tool kickoff, T0/T1 sampling, tuning, and PPAP — reviewing mold flow, tool design, and first-shot parts, and driving corrective action.
Build and test prototypes using 3D printing, urethane casting, CNC, and soft tooling, then validate with drop, structural, and environmental testing.
Exceptional mechanical intuition. You understand how polymers flow, shrink, warp, and fail — and you can predict it before the tool is cut.
5+ years designing injection-molded plastic parts for high-volume consumer, automotive, or robotics products, with parts you took from CAD through production tooling.
Deep, hands-on knowledge of polymer materials science: resin families, fillers and additives, anisotropy, moisture sensitivity, and how process conditions change part behavior.
Fluent in GD&T (ASME Y14.5 or ISO GPS) and tolerance analysis, including worst-case and statistical stack-ups on compliant plastic parts.
Proven experience owning cosmetic A-surface parts and defending appearance standards with vendors.
Strong surfacing skills in SolidWorks, CATIA, NX, Creo, or similar; comfort reading mold flow results and tool drawings.
Comfortable with ambiguity, moves fast, and curious about how the whole system works.
About Mind:
Mind Robotics is building Physical AI for real-world industrial deployment, starting with the factory floor. We believe the hardest problems in AI are solved when researchers and engineers are hands-on with the physical world every day - and we're looking for people who are passionate about robotics, value ownership, and are excited to tackle difficult problems. Join us if you want to move beyond digital intelligence and put intelligence into motion.
About the team and the role:
We're building robots for industrial deployment that push the limits of what hardware can do - and the electronics inside them have to survive the real world, at scale, from suppliers who've never built anything quite like this before. We need someone who has industrialized components in the trenches: walked the SMT lines, run the PPAPs, and carried a design from first prototype to a ramp that didn't fall over.
You'll help build our supplier industrialization function from scratch - the process, the supplier base - while staying hands-on with the parts. You'll own PCBA, electronics, and tactile sensing to start, embedded with our engineering team from the first schematic review. As we scale, so does the scope: this role can grow into leading the SIE organization, or into a senior technical authority on manufacturability for someone who wants to stay deep. We're open to both.
Responsibilities:
Build the supplier industrialization function from the ground up: define the SI playbook, stage-gate requirements, supplier readiness criteria, and the full PPAP/qualification process (control plans, pFMEA, MSA/Gage R&R, capability studies, first-article inspection, run-at-rate) appropriate for a company moving from prototype builds to volume production
Own industrialization end-to-end for PCBAs, electronic assemblies, and tactile sensing components, from supplier selection through process development, qualification, launch, and ramp
Engage with electrical, mechanical, and firmware engineering from the design phase: drive DFM/DFA/DFT reviews, influence component selection, stack-ups, panelization, and test strategy early enough to actually change the design, not just react to it
Identify, assess, and select suppliers: lead technical audits of manufacturing capability, quality systems, process maturity, and capacity; make clear-eyed calls on who can scale with us and who can't
Travel to supplier sites (CMs/CEMs, PCB fabs, sensor manufacturers) to develop processes, validate lines, resolve issues, and verify manufacturing readiness firsthand
Lead new product introduction at supplier sites: process development and validation, SMT line bring-up, test correlation, yield improvement, and launch readiness for each build stage (EVT/DVT/PVT to production)
Develop and execute the supply chain readiness plan to scale electronics from tens of prototype units to hundreds and thousands of production units, including tooling, test fixtures, and capacity planning
Establish supplier quality management: incoming quality criteria, SPC deployment, root-cause/corrective action (8D/RCCA) on process and field failures, and ongoing supplier performance reviews
Work hand-in-hand with our sourcing/GSM function on supplier strategy, should-cost input, make-vs-buy decisions, and risk mitigation (dual-sourcing, alternate qualification)
Set the foundation for the SI team: define hiring needs by commodity as we grow, and build the systems and documentation that let the function scale beyond one person
Requirements:
You've built or significantly shaped an SI, SQE, or NPI function before. You know what it means to operate without a playbook because you've written one, not just followed it
7+ years in supplier industrialization, supplier quality, NPI, or manufacturing engineering in hardware-intensive industries such as robotics, consumer electronics, automotive, or aerospace
Working knowledge of IPC standards and PCB fabrication processes; experience with flex and rigid-flex circuits or sensor integration (tactile, force, capacitive, camera modules) is a strong plus
Fluent in the industrialization toolkit: PPAP, APQP, pFMEA, control plans, SPC, MSA/Gage R&R, DOE, capability analysis (Cpk), 8D/RCCA
Hands-on experience taking products through the prototype-to-production transition, including EVT/DVT/PVT builds, tooling and test fixture qualification, run-at-rate, and ramp
Engineering degree (Electrical, Mechanical, or similar) and the ability to read schematics, layouts, and drawings well enough to hold your own in design reviews with the engineers who made them
Experience auditing and developing suppliers directly, including CMs/CEMs and component manufacturers worldover
Comfortable operating as both architect and operator: setting up systems one day, debugging a PCBA failure at a supplier the next
You thrive in ambiguity, default to urgency, and have the drive to build - not just maintain
Experience with tactile sensing or other high-mix sensor manufacturing (is a plus)
Experience in humanoid or mobile robotics, or other high-complexity electromechanical systems (is a plus)
Six Sigma / Lean certification, ASQ CQE, or equivalent (is a plus)
Experience hiring and leading the SIE or SQE function (is a plus)
About Mind
Mind Robotics is building Physical AI for real-world industrial deployment, starting with the factory floor. We believe the hardest problems in AI are solved when researchers and engineers are hands-on with the physical world every day - and we're looking for people who are passionate about robotics, value ownership, and are excited to tackle difficult problems. Join us if you want to move beyond digital intelligence and put intelligence into motion.
About the Role
As an Actuator Test Engineer on our team, you will build the dynamometer test infrastructure to verify how every actuator we put on the robot actually performs. You will move fluidly between designing test fixtures, building test stands, and running the characterization tests that drive our actuator selection and sizing decisions.
Responsibilities
Design and build the dynamometer test systems, including mechanical driveline, load application, and sensor integration (torque, position, thermal).
Design and fabricate test fixtures and stands for actuator, gearbox, and driveline validation, producing accurate 2D drawings with GD&T (ASME Y14.5) for tight tolerances and proper fits.
Run tests on actuators to characterize torque, speed, efficiency, thermal performance, stiffness, friction, bandwidth, life-cycle, backlash, and torque sensor accuracy.
Support electromechanical integration of motors, encoders, torque sensors, and other components onto test rigs.
Generate and visualize the data that drives actuator selection and sizing decisions
Build repeatable test procedures and data pipelines that scale from a single actuator on a bench to characterizing units across the fleet.
Quantify unit-to-unit variation, track drift and wear over time, and flag outlier units.
Perform post-test teardown inspections (gear wear, bearing wear, lubrication condition, wiring/sensor integrity) and document findings rigorously.
Qualifications
3+ years of hands-on experience testing and characterizing real electromechanical or robotic hardware.
Demonstrated ability to design and build test hardware such as fixtures, stands, and drivelines from concept through fabrication.
Proficiency with 3D CAD and the ability to generate 2D drawings with GD&T per ASME Y14.5.
Solid grasp of actuator fundamentals: motors, gear transmissions (planetary, harmonic/strain-wave, cycloidal), encoders, torque/force sensors, and thermal behavior.
Experience with dynamometer setups or equivalent custom test rigs, from concept through fabrication.
Bachelor's or Master's Degree in Mechanical Engineering, Electrical Engineering, Mechatronics, or a related field — or equivalent hands-on experience.
Ability to write and maintain clean test documentation and procedures.
Bonus Qualifications:
Experience with planetary, harmonic/strain-wave, and cycloidal actuators specifically, and familiarity with the tradeoffs between them.
Experience scaling test or calibration pipelines across a large fleet of test systems.
Manufacturing process familiarity and first-article inspection experience.
Experience with CATIA V6
Mind Robotics is building intelligent robotic systems. Our work happens on-site across active hardware labs, test floors, and compute environments where hardware and software iterate together daily.
As our first dedicated IT hire, you will build and own our IT operations end to end. You’ll be responsible for making sure our teams never lose momentum to tooling, identity, device, or network issues, whether that means troubleshooting a Linux/driver setup in the lab, setting up zero-touch laptop provisioning for new hires, or ensuring our test-floor Wi-Fi can handle high-throughput robot telemetry.
We believe modern IT should be built for extreme leverage. In this role, you will actively use AI agents and build agentic workflows (leveraging LLMs, MCP, and API integrations) to automate support intake, triage, provisioning, access approvals, and system reconciliation. You'll build an AI-native IT foundation that keeps operations fast, lean, and scalable without scaling support headcount.
Own internal IT & support: Be the primary technical point of contact for the team. Establish a lightweight, responsive support workflow that unblocks engineers quickly without unnecessary red tape.
Build agentic automations & workflows: Leverage AI coding agents and LLM-driven workflows (via APIs, MCP, webhooks) to automate repetitive IT operations: such as intelligent ticket triage, access requests, onboarding/off-boarding, and system reconciliation.
Manage identity, access, and endpoints: Administer our core SaaS, identity, and access stack (Google Workspace / IdP) and deploy modern MDM (e.g., Fleet, Jamf) for zero-touch provisioning and fleet visibility across macOS, Windows, and Linux machines.
Support robotics lab & compute infrastructure: Help maintain our lab workstation fleet (Ubuntu boxes, test bench PCs), keeping configurations reliable and reproducible.
Design and maintain office & lab networking: Manage local network infrastructure - VLAN segmentation (isolating robot telemetry, test benches, and corporate traffic), Wi-Fi reliability in dense RF lab environments, managed switches, and ISP circuits.
Manage hardware & procurement: Own hardware ordering, inventory tracking, vendor relationships, conference room AV, and hands-on physical lab IT setup (racking, cabling, and workstation rollouts).
Experience: 6+ years of hands-on IT operations, systems administration, or IT engineering experience, ideally in a fast-paced startup, hardware, or robotics environment.
AI & Agentic fluency: Active user of AI coding tools and agents (Cursor, Claude Code, LLM APIs) to accelerate development, with experience or strong interest in building agentic/LLM-assisted workflows for operations.
Multi-OS troubleshooting: Strong troubleshooting skills across macOS, Windows, and Linux (Ubuntu) at the OS, driver, and application layers.
Networking fundamentals: Practical hands-on experience configuring managed switches, subnets, VLANs, DNS/DHCP, VPN, and robust Wi-Fi networks.
AI & MCP integrations: Experience deploying LLM agents, MCP servers, or AI-native internal tools using Python, JS, Git, or Bash for IT/Ops automation.
Identity & MDM: Hands-on experience administering enterprise identity (Google Workspace, Okta) and modern MDM platforms (Fleet, Jamf, Intune, or equivalent).
Hands-on facility readiness: Willingness to work on-site and handle physical infrastructure: racking equipment, running and terminating cable, and supporting all types of hardware in an active lab and office environment.
Robotics & ML: Experience supporting robotics environments, ROS, or GPU/CUDA machine learning workstations (is a plus).
Facility scaling: Experience managing IT infrastructure through a new facility buildout or office/lab move (is a plus).
Mind Robotics is building Physical AI for real-world industrial deployment, starting with the factory floor. We believe the hardest problems in AI are solved when researchers and engineers are hands-on with the physical world every day - and we're looking for people who are passionate about robotics, value ownership, and are excited to tackle difficult problems. Join us if you want to move beyond digital intelligence and put intelligence into motion.
At Mind Robotics, we're building generalized physical AI — robotic systems capable of dexterous, adaptive, and reasoning-intensive work in real-world industrial environments. That depends on a runtime platform that lets robots perceive, decide, and act under hard real-time constraints.
We're looking for a Tech Lead to lead the development for this runtime: how the robotics and AI modules communicate and execute under tight latency budgets. This is an IC-heavy role: you're in the codebase daily, making the hard latency and correctness tradeoffs yourself, not delegating them.
This is a role for someone who wants to own the hardest problems in real-time robotics software and onboard model serving, and ship them personally.
Design and build the robotics runtime/middleware layer – including topic/message architecture, QoS policies, and multi-process communication under tight latency budgets
Own the inference-serving path for onboard models (VLA/action-expert policies): batching, quantization, hardware acceleration, and the tradeoffs between model accuracy and control-loop latency
Architect multi-sensor synchronization and fusion arriving on different clocks and cadences, kept coherent enough for closed-loop control
Make and own the hard technical calls on middleware choice, process/thread architecture, and how much real-time guarantee any given subsystem actually needs
Set technical standards and do deep design/code review across runtime engineering
Prototype and de-risk new architecture directions (new middleware, new compute hardware, new model-serving approaches) before they become team-wide commitments
Work directly with modeling/research to understand what a policy actually needs from the runtime (latency, synchronization, action representation) and make sure the platform delivers it
Strong systems programming in Python and/or Rust
Hands-on experience with robotics middleware — ROS2 (rclcpp/rclpy, DDS implementations like Fast DDS/CycloneDDS, or Zenoh), including designing custom message types, QoS tuning, and multi-node communication patterns
Real-time systems experience: understanding of scheduling, latency budgets, jitter, and the difference between soft and hard real-time guarantees; comfort with RTOS concepts
Experience with embedded/edge Linux — cross-compilation, device driver basics, and resource-constrained execution (CPU/memory/power budgets)
Multi-sensor synchronization: time sync protocols (PTP/gPTP), sensor fusion pipelines, and handling clock drift/skew across distributed compute on a single robot
Model deployment/inference optimization: exporting and running models via ONNX Runtime, TensorRT, or similar; quantization and batching strategies for action-model inference on edge compute; understanding of how model latency translates into control-loop constraints
Experience with control systems and motion primitives — enough to reason about how software architecture decisions (message latency, action chunking, control frequency) affect closed-loop robot behavior
Track record of taking real-time systems from prototype to reliable, continuous operation on physical hardware
Strong technical judgment on tradeoffs between research/experimentation velocity (fast iteration, permissive interfaces) and runtime reliability (determinism, fault tolerance, safety margins)
Comfort making build-vs-buy calls on middleware and compute hardware
Clear communicator who can work directly with research/modeling partners to translate model requirements into runtime architecture, without a layer of translation in between
Familiarity with simulation environments (Isaac Sim, MuJoCo, Gazebo) for validating runtime behavior before field deployment
Exposure to functional safety concepts relevant to physical systems
Background in autonomous vehicles, industrial IoT, or other domains with similar real-time/embedded constraints
Experience scaling a runtime platform from a handful of robots to many concurrent units in production
Some prior experience providing technical mentorship or informal lead direction to other engineers, without formal management responsibility
About the team:
Mind Robotics is building robots that learn from real-world experience — and the quality of that experience starts with how we collect and process data. Our field data collection system puts capture rigs on the factory floor at manufacturing sites, generating the raw sensor data that trains everything downstream. Our teleoperations program turns human demonstrations into high-quality data that grounds our AI models in physical reality. Both flow through annotation and quality control into our training platform.
About the role:
As a Software Engineer on Edge & Field Systems, you'll be one of the founding engineers on the team that owns our application layer, reporting to our Integration Manager. The systems work today; your job is to help harden and productionize them for scale — from 10 capture stations at one site to hundreds across multiple OEM plants, and from a handful of teleop stations to a multi-shift fleet. This is early, hands-on, 0-to-1 engineering: you'll ship code that runs on real hardware in real factories, and see its effect on robot behavior.
You will:
Build the field capture stack — edge software for our data collection rigs: device management, sensor orchestration, and real-time data quality monitoring that holds up on a factory floor, with workflows optimized for I/O-, compute-, and network-bound constraints
Ship the teleop stack — collection, evaluation, and operator tooling for our teleoperation stations, including metrics, task management, and the workflows our robot operators use every day
Build and contribute to integration and validation testing for field software, including hardware-in-the-loop (HIL) test setups that catch failures before they reach a live site
Build monitoring and dashboards that give visibility into fleet health, capture quality, and station uptime across sites, accounting for intermittent connectivity and bounded bandwidth in the field
Make deployment repeatable — build tooling and configuration systems that make onboarding a new manufacturing site a config change, not a custom engineering project, including device provisioning workflows
Support the field — debug issues on live systems, instrument for observability, and work directly with site operations staff and robot operators who depend on your software
Requirements:
2+ years of software engineering experience building production systems
Strong programming fundamentals and comfort working across the stack — edge devices, services, and local data pipelines
Experience with at least one of: edge computing, device or fleet management, real-time systems, robotics or sensor systems
Bias for ownership: you've taken features or systems from prototype to production and supported them in the field
Clear communication and close collaboration with product, research, and operations partners
Hands-on experience with sensor data (video, depth, IMU, force/torque) and the infrastructure to collect and monitor it in real time is a plus
Experience with hardware-in-the-loop or simulation-based testing (e.g., Isaac Sim, Gazebo, MuJoCo) is a plus
Background in robotics, autonomous vehicles, industrial IoT, or teleoperation systems is a plus
Exposure to manufacturing or other industrial environments is a plus
Mind Robotics is building Physical AI for real-world industrial deployment, starting with the factory floor. We believe the hardest problems in AI are solved when researchers and engineers are hands-on with the physical world every day - and we're looking for people who are passionate about robotics, value ownership, and are excited to tackle difficult problems. Join us if you want to move beyond digital intelligence and put intelligence into motion.
Responsibilities
Own the mechanical architecture of Mind's wearable data collection platform across both head-worn and body-worn devices, including what carries forward between generations and what gets rebuilt.
Set the mechanical requirements the optics, electrical, and compute teams design against: volume and keep-out budgets, alignment and stiffness targets, thermal limits, and mass allocation. Own the tradeoffs when those budgets conflict, and decide which ones give.
Decide which mechanical problems each device generation takes on and which it deliberately does not, and hold that line when the program pushes back.
Own build and tooling strategy for the platform: what is printed, machined, molded, or bought; when tooling gets committed; and which molders, soft goods vendors, and CMs Mind builds a long-term relationship with.
Set the mechanical standards other hardware teams work to: design review, tolerance analysis practice, materials and skin-contact qualification, and DFM expectations for vendors.
Stay hands-on. Build, test, and break prototypes on real users, and personally run the tolerance, structural, drop, and thermal analyses that decide the architecture.
Grow the mechanical bench through design review, mentorship on the wearable stack, and hiring.
Qualifications
Exceptional mechanical intuition. You understand how forces, materials, and mechanisms behave instinctively.
You have owned the mechanical architecture of a shipped wearable, head-worn, or handheld consumer device end to end, and can describe both the tradeoffs you chose and the ones you refused.
Depth packaging cameras, sensors, compute, and batteries into space-constrained enclosures with tight tolerance and alignment requirements, with a track record of setting those budgets rather than working inside someone else's.
Deep hands-on knowledge of injection molding and polymer selection, from part design through tooling and T1 to production, including judgment on when to commit tooling.
Working fluency in soft goods and textiles: straps, padding, fabric-to-rigid interfaces, and skin-contact ergonomics. You know when this needs a specialist and can direct one.
High proficiency in SolidWorks, Catia, NX, or similar CAD tools, including surfacing.
Comfortable with ambiguity, moves fast, and curious about how the whole system works.
Mind Robotics is building Physical AI for real-world industrial deployment, starting with the factory floor. We believe the hardest problems in AI are solved when researchers and engineers are hands-on with the physical world every day - and we're looking for people who are passionate about robotics, value ownership, and are excited to tackle difficult problems.
The Data Collection team owns the raw material our foundation models are trained on. We pilot robots through real industrial manipulation work. The skills could be pick and place, sorting, fastening, seating connectors but then we turn those hours into the demonstration data our models learn from. With Rivian as our first manufacturing partner, we are emulating the live, high-variability work inside an active production facility.
As a Robot Operator, you are the person actually showing the robot how the work is done. You'll spend your day on the floor running robots through tasks, some routine, some brand new, some that don't work yet and need figuring out. Along the way you will be developing judgment about what separates a good demonstration from a bad one. You'll be closer to the hardware than anyone at the company, which means you'll be the first to know what's awkward, what's unsafe, and what should change. We're early, so the process you learn this month may get rewritten next month, usually because someone on the team found a better one.
Pilot the robots. Operate leader/follower rigs and other control setups to run robots through industrial manipulation tasks: pick and place, bin sorting, fastener driving, connector seating, tool use.
Produce data the models can learn from. Execute repeated demonstrations to a consistent standard — clean motion, consistent framing, sensible recovery when something goes wrong — and develop the eye to tell a usable episode from an unusable one.
Own the quality of your own output. Review, label, and flag your own bad episodes rather than letting them through, and keep clear session logs another person can act on.
Work safely, so we can work fast. Run pre-power-up safety checks, know where the stop is, follow lockout procedure, and raise concerns early. Careful people let us move quickly.
Run new tasks from a cold start. Take an unproven task description, work out a repeatable approach on the hardware, and document what you found.
Feed findings back into how the work is designed. Report rig ergonomics problems, ambiguous instructions, and task setups that don't survive contact with reality to the engineers and researchers who own them.
Help the next person. Train new operators on tasks you've mastered and sharpen the written procedures as you go. There's a clear path toward owning task areas and training operators — that's the growth we expect, not a day-one expectation.
Expect to be on your feet and moving for roughly 5–6 hours of the day. Operating the rigs is sustained, full-body work: reaching, bending, crouching, twisting, holding position, and extended use of both arms and hands. Come ready for that.
This is not a lifting role. There is no significant lift or carry requirement. The demand is sustained manipulation and posture, not strength.
We can accommodate seated and wheelchair-based operators. Our control setups can be operated seated, and we're willing to adapt rigs, station height, and task assignment to make that work. If you use a wheelchair or have other accessibility needs, this role is open to you.
Roughly one year or more of work history where you were accountable for your own output: showed up on time, held to a standard, dealt with the consequences when something went wrong. The industry doesn't matter; having been held to a bar does. No degree required — relevant hands-on experience or equivalent experience is what counts.
Genuine interest in robots and AI. You don't need expertise. You do need to want to know how the thing works, and to get a little excited when it does something new.
A demonstrated safety mindset. Steady around powered equipment, follows procedure without being watched, comfortable stopping work to raise a concern.
Strong hand/eye coordination and spatial reasoning. You pick up physical skills quickly and can explain what makes one approach better than another.
Tolerance for high-repetition work at a consistent quality bar across a full shift.
Clear, plain written communication. Enough to log what happened in a session so someone else can understand and act on it.
Comfort with direct feedback, both getting it and giving it.
Tolerance for change. Early-stage means unfinished. If that sounds fun rather than stressful, that's a good sign.
Authorization to work on-site in Palo Alto, CA for a role that cannot be performed remotely.
Experience with humanoid or mobile robots, VR/AR systems, or motion capture is a plus
Exposure to how ML training data is produced is a plus
Light scripting (Python or shell) is a plus
Teaching or coaching experience is a plus
Shift work in a safety-sensitive setting is a plus
Time in a manufacturing or production environment is a plus
About Mind:
Mind Robotics is building robots that learn from real-world experience, and the data platform is what turns raw experience into training signal. Every day, sensor data flows in from multiple sources — arriving in different formats, at different volumes, and with different quality bars. This team owns what happens next: ingesting that data reliably, validating and curating it for quality and diversity, powering annotation, and building the automated methods that will increasingly do that labeling work.
About the role:
As a Software Engineer working on the data platform, you'll be one of the founding engineers on the team that owns our application layer, reporting to our Head of Application Engineering. The systems work today; your job is to help harden and productionize them for scale — ingesting from a growing number of sources and field sites, and turning raw captures into training-ready data faster and at higher quality. This is early, hands-on, 0-to-1 engineering: you'll build the pipelines and tooling that determine what data actually makes it into our models, and see the effect on robot behavior.
You will:
Build data ingestion pipelines from multiple sources, including our own field capture and teleop stacks
Build automatic data validation systems that catch quality issues before data reaches annotation or training
Build and improve annotation ingestion, tooling, and workflows to increase labelling efficiency and throughput
Own data quality and diversity — build the systems and metrics that tell us what data we have, what's missing, and where to focus collection
Explore and build automated annotation methods, including running computer vision and vision-language models for tasks like depth estimation, hand tracking, and auto-labeling, to reduce manual labelling load over time
Support the platform — debug issues in live pipelines, instrument for observability, and work directly with research and annotation partners who depend on your software
Requirements:
2+ years of software engineering experience building production systems
Strong programming fundamentals and comfort working across the stack — services, data pipelines, and applied ML tooling
Experience with at least one of: real-time or large-scale data pipelines, ML data workflows, computer vision, or data infrastructure
Bias for ownership: you've taken features or systems from prototype to production and supported them in the field
Clear communication and close collaboration with product, research, and annotation/operations partners
Hands-on experience with sensor data (video, depth, IMU, force/torque) and the infrastructure to process, validate, and label it at scale is a plus
Experience with streaming or near-real-time data pipelines is a plus
Familiarity with ML data workflows (datasets, labelling, evaluation) is a plus
Experience running or integrating computer vision or vision-language models (e.g., depth, pose/hand tracking, open-vocabulary detection, auto-labeling) is a plus
About Mind:
Mind Robotics is building Physical AI for real-world industrial deployment, starting with the factory floor. We believe the hardest problems in AI are solved when researchers and engineers are hands-on with the physical world every day - and we're looking for people who are passionate about robotics, value ownership, and are excited to tackle difficult problems. Join us if you want to move beyond digital intelligence and put intelligence into motion.
About the team and the role:
We're looking for a senior communications leader to serve as our first dedicated comms hire and primary partner to our CEO. You will own both his public profile as it relates to Mind Robotics – earned media, podcast placements, press interviews, and executive voice – and the company's internal communications as we scale. You are a builder who can operate without a team, move fast, and do the work. This role will shape the narrative and public opinion for Mind Robotics’ next phase of growth.
Responsibilities:
CEO & Executive Communications
Serve as the CEO's day-to-day communications partner: briefing docs, media prep, talking points, and message development for all public appearances.
Drive a proactive earned media strategy—pitch and secure placements on high-impact podcasts, business and tech press, and conference stages.
Own the CEO's editorial output: op-eds, public statements, and investor-facing communications.
Build and maintain relationships with journalists, podcast producers, and editors on the CEO's behalf.
External & Corporate Communications
Develop and own the company's messaging architecture, narrative positioning, and crisis communications framework.
Lead all media relations: serve as primary press contact, manage inbound inquiries, and produce press materials for key milestones.
Support fundraising and product launch communications in partnership with leadership, finance, and legal.
Internal Communications
Design and run the company's internal communications infrastructure: all-hands formats, leadership updates, and the cadences that keep a growing team aligned.
Write company-wide announcements, organizational change communications, and culture moments in a consistent, on-brand voice.
Partner with HR and People Ops on employee experience communications as the team scales.
Requirements:
External
CEO media strategy: Develop a 6-month earned media roadmap with target outlets, podcast targets, and placement calendar within first 30 days.
Press kit & messaging house: Build a foundational messaging architecture and press kit (company backgrounder, CEO bio, boilerplate, FAQs) within first 45 days.
First placements: Secure at least 2–3 meaningful CEO media placements (podcast appearances or press features) within the first 90 days.
Launch playbook: Partner with Rivian's communications team to co-develop artifacts, playbooks, and SOPs for initial operator deployments — establishing a repeatable framework for all future launch communications.
Internal
All-hands framework: Audit existing all-hands format and deliver a redesigned structure—agenda, narrative arc, CEO prep guide—within first 30 days.
Internal comms cadence: Stand up a recurring internal newsletter or leadership update channel (Slack, email, or equivalent) by day 60.
New hire communications: Develop onboarding communications materials that orient new employees to company mission, culture, and leadership voice.
Cultural moment coverage: Own communications around at least one major internal milestone (hiring milestone, product update, company offsite) end-to-end within the first 6 months.
Qualifications:
8–12+ years in communications, with significant in-house experience at a technology company; Director-level or equivalent.
Proven track record managing executive communications for a founder-CEO or senior leader with an active public profile.
Strong media relationships across tech and business press; existing podcast producer and digital media contacts a strong plus.
Exceptional writer across all formats—press releases, executive narrative, internal memos, and editorial content.
Startup builder mentality: comfortable owning a function end-to-end, creating structure from ambiguity, and operating without a team.
Experience in deep tech, robotics, automotive, or adjacent industries preferred.