Senior Software 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 - our rapidly developed humanoid platform being deployed in real industrial environments - and we’re growing the team to take it even further.

We are looking for a Software Engineer to join our Control Team in London.

You will own how we measure control quality - per robot and across the fleet. Today, judging whether a change made the robot better is still too often an engineer's opinion after a few runs. You will replace that with numbers: reproducible, automated, trusted numbers that say whether a release is better than the last one, whether one robot behaves like another, and whether a fleet is ready for a pilot.

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  Turn qualitative concerns about control quality - smoothness, latency, tracking error, accuracy, and the like - into precise, computable metrics, and build the pipelines that compute them consistently from recorded robot data, simulation runs and live telemetry.

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  Own metric definitions and versioning so a number measured today is comparable with the same number measured six months from now.

• Develop automated test harnesses that evaluate control releases deterministically - reproducible, run-to-run comparable, free of the hidden state that makes hardware testing unreliable.

• Wire control evaluation into CI across simulation and hardware-in-the-loop, with pass/fail thresholds so regressions are caught automatically rather than discovered on a customer site.

• Build the tooling that lets engineers compare controller variants and releases side by side.

• Build the per-robot and per-fleet test suites that establish whether each robot meets the expected control performance bar - runnable by other teams without controls support.

• Track control quality over time and across units - drift, per-robot variation, degradation - and own the path from robot to metric, including the dashboards that make control health legible.

• Robotics experience on real systems - you understand robot data, timing and synchronisation, and the many ways hardware experiments go wrong.

• Strong Python, plus enough modern C++ to work confidently inside a real-time control codebase and instrument it.

• Built test automation, evaluation frameworks or benchmarking infrastructure for complex systems - and got other engineers to actually use them.

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

• Experience with robot middleware and data formats (ROS/ROS2, message recording and replay, time sync), CI/CD pipelines and Git-based workflows.

Nice to have:

• Familiarity with robot kinematics and dynamics, or prior exposure to control systems — enough to hold an informed conversation about what you are measuring.

• Hardware-in-the-loop test rigs and physical regression testing.

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

• Operating or supporting a fleet of robots or other distributed physical devices.

• Data engineering: pipeline orchestration, time-series databases, large-scale log processing.

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