Robotics Controls Engineer

Here at Humanoid, we believe in a future where robots amplify human potential. That’s why we’ve set out on a mission to build the world’s most capable, commercially-scalable, and safe humanoid robots. We’re bringing that mission to life with HMND‑01 Alpha - our rapidly developed humanoid platform now running in real industrial pilots - and we’re growing the team to take it even further.

We are looking for a Controls Engineer to join our Control Team in Boston.

You will own how we know our controls stack works: the testing and validation infrastructure that turns "it looked fine on the robot" into measured, reproducible evaluation, the observability and failure analysis that makes runtime behaviour visible and failures shareable, and the documentation and runbooks that let the team self-serve without institutional knowledge. You will also strongly contribute to first-time bring-up of new hardware for Boston components and validate changes end-to-end on real robots before the wider team depends on them.

The ideal candidate has strong control fundamentals, solid software engineering discipline across the stack - from control code to test harnesses to diagnostic tooling - and the debugging instincts that only come from real hardware. You will work across the whole control stack and be the person the team relies on to tell them the truth about their code.

Testing & Validation Infrastructure:

• Build and own hardware-in-the-loop (HIL) and integration test workflows that quantify controller and motion repeatability and accuracy - turning "it looked fine on the robot" into measured, reproducible evaluation.

• Define and implement repeatability and accuracy metrics for controller and motion changes, and the harnesses that make those measurements consistent across runs and across robots.

• Expand unit and integration test coverage across the controls components the team actually runs - actuator drivers, hardware interfaces, whole-body control, and the teleoperation pipeline.

• Develop automated regression checks that surface controls regressions - in accuracy, repeatability, or behaviour - in simulation and on hardware before they reach the wider team.

• Work with controls engineers to define testability requirements for new components and algorithms, so things are built to be evaluated and validated.

• Package controllers into cleanly separable, reusable, and easily testable interfaces - so individual controllers can be developed, swapped, and validated in isolation.

New Hardware Bring-Up & Integration:

• Strongly contribute to first-time bring-up of new hardware for Boston components, and to their integration into the controls stack.

• Validate hardware and software changes end-to-end on real robots before broader team use - structured, documented, and repeatable.

Debugging, Observability & Failure Analysis:

• Instrument the controls stack to improve visibility into runtime behaviour - logging, tracing, and diagnostic tooling.

• Reproduce hardware and software failures in structured, developer-shareable formats; feed structured root-cause context back to algorithm and hardware owners.

• Identify and resolve cross-stack failure modes that span controls, firmware, and hardware.

Documentation & Knowledge Transfer:

• Write and maintain engineering documentation for the controls stack: HIL and test coverage, and bring-up/integration guides for new hardware.

• Own runbooks that let controls engineers self-serve validation, integration, and recovery without relying on institutional knowledge.

• Substantial hands-on robotics experience on real hardware.

• Strong control theory foundation (joint-space control, PID and feedforward, impedance/admittance, observers, state estimation, filtering, stability) — enough to define meaningful repeatability and accuracy metrics for a control stack and interpret what they say.

• Shipped software across the stack: modern C++ inside a real-time control codebase, plus Python for test harnesses, analysis and tooling.

• Built test automation, HIL or integration-test infrastructure for complex physical systems — and got other engineers to actually use it.

• Solid measurement and data analysis judgement: sampling, timing and synchronisation, noise, statistical significance, and knowing when a difference between two runs is real.

• Strong debugging across software/firmware/hardware boundaries, with the discipline to reproduce failures in a form other engineers can act on.

Nice to have:

• Physics simulation environments (MuJoCo, Isaac Sim/Isaac Lab) and sim-to-real validation.

• Observability tooling: logging and tracing frameworks, metrics collection, time-series databases, dashboarding (e.g. Grafana).

• First-time hardware bring-up experience: new actuators, sensors or compute targets integrated into a control stack.

• A track record of engineering documentation and runbooks that outlived their author.

• Release management or quality gates for safety-relevant software.

• Comprehensive health coverage for US‑based employees, including fully paid medical, dental, and vision insurance, with virtual care and employee assistance resources.

• Meaningful time off to rest and recharge: 23 days of PTO (accrued), separate sick leave, and paid company holidays.

• 401(k) retirement plan with 4% employer match.

• Competitive equity: stock options with meaningful upside as we scale.

• Free daily catered lunch, snacks, and drinks in‑office.

• Collaboration with top‑tier engineers, researchers, and product experts in AI and robotics.

• Freedom to influence the product and own key initiatives.