Robotics Device Software Engineer — Sensor Data & Pipelines

Humanoid

United Kingdom

On-site

GBP 60,000 - 90,000

Full time

14 days+
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Benefits offered by this job

Equity options
Paid time off
Private healthcare
Pension 8%
Office meals
World-class team

Job summary

Humanoid is developing HMND‑01 Alpha, an advanced humanoid platform, and seeks a Software Engineer to own device software and data pipelines for UMI data collection devices. This hands‑on role involves building production‑grade software, sensor fusion and validation tooling, and close collaboration with hardware, AI, and data teams.

You’ll work on episode lifecycle software, sensor integration, data contracts, and field data collection validation, ensuring high data quality for training

Qualifications

  • Experience in embedded or device-level software on hardware (not just talking to it).
  • Proficiency in C++ and/or Python for real-time sensor drivers and data pipelines.
  • Solid understanding of multi-modal sensor synchronisation and timing issues.
  • Knowledge of camera pipelines and data landing with correct timing.

Responsibilities

  • Develop and maintain device software across the full episode lifecycle (initialisation, per-sensor streams, recording, shutdown).
  • Integrate sensor streams (cameras, pose trackers, encoders, and optional force/torque sensors) into synchronized trajectories.
  • Define data contracts between devices and AI team: stream formats, metadata, and training pipeline interface.
  • Build data validation to detect dropped frames, jitter, pose loss, image quality issues, and sensor faults.

Job description

Humanoid is developing HMND‑01 Alpha, an advanced humanoid platform, and seeks a Software Engineer to own device software and data pipelines for UMI data collection devices. This hands‑on role involves building production‑grade software, sensor fusion and validation tooling, and close collaboration with hardware, AI, and data teams.

You’ll work on episode lifecycle software, sensor integration, data contracts, and field data collection validation, ensuring high data quality for training

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