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 Software Engineer to own the device software and data pipeline for our UMI data collection devices - handheld gripper systems used by human operators to demonstrate manipulation tasks. The data they produce is used to train our robots, so the quality of what you ship determines the quality of robots we deploy.
This is a deeply technical, hands-on role. You will write production-grade device software, build reliable sensor fusion and recording pipelines, and own the validation tooling that keeps our training datasets clean. You will work closely with hardware, AI, and data collection teams.
Develop and maintain device software across the full episode lifecycle: session initialisation, per-sensor stream management, episode recording, and graceful shutdown
Integrate and synchronise sensor streams - wrist and head cameras, 6-DOF pose trackers, gripper aperture encoders, and optionally force/torque sensors - into temporally-aligned, policy-consumable trajectories
Define and maintain the data contract between UMI devices and the AI team: stream formats, episode packaging, metadata schemas, and the interface between raw recordings and training pipelines
Build and maintain data validation tooling that detects dropped frames, timing jitter, pose tracking loss, image quality degradation, and sensor faults before corrupt episodes enter the training dataset
Own operator-facing device software: status indication, start/stop/cancel episode controls, and clear feedback when a session needs to be retried
Support field data collection sessions - deploying devices to operators, validating data quality in situ, and iterating quickly when issues surface during collection
Debug integration failures on device hardware: camera driver issues, bus bandwidth saturation, tracker calibration drift, compute thermal throttling, and storage write failures mid-episode
Experience in embedded or device-level software in a robotics or sensing context - writing code that runs on the device, not just talking to it
Proficiency in C++ and/or Python for real-time sensor drivers, inter-process communication, and data recording pipelines
Solid understanding of multi-modal sensor synchronisation - timestamps, hardware triggers, clock drift - and the practical consequences of getting it wrong for downstream ML
Familiarity with camera pipelines and the difference between what a sensor reports and what actually lands on disk with correct timing
Strong instincts around data quality and dataset hygiene - you are uncomfortable shipping episodes with frame drops, tracking loss, or misaligned streams
Proven ability to debug on real hardware: you read logs, attach profilers, and can diagnose whether a problem is a driver, a bus, or a timing issue
Experience with 6-DOF pose tracking systems (VIO, optical trackers, or similar) and their common failure modes
Familiarity with ML training data formats and what an AI team actually needs from a recorded episode - understanding the consumer of the data you produce
Exposure to operator-facing tooling for data collection: session management, per-episode quality summaries, rejection and retry workflows
Experience scaling device software from a small number of prototype units to many devices operated by non-engineers in the field
Meaningful time off to rest and recharge: 23 days of annual leave (accrued), 15 days of paid sick leave, and paid company holidays.
Fully funded private healthcare for UK employees, with broad provider access, virtual and in‑person care, and strong mental health and serious illness support.
Equity included–we believe builders should share in what they build.
Pension scheme with a total 8% contribution (5% employee, 3% employer) on full earnings.
Free daily breakfast, catered lunch, and snacks in‑office.
Collaboration with top‑tier engineers, researchers, and product experts in AI and robotics.
Freedom to influence the product and own key initiatives.
