Senior Autonomy Engineer (SLAM & Navigation)

humanoid
London

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.

About the Role

We're hiring a Senior Autonomy Engineer (SLAM & Navigation) to join our team. In this role, you will bridge the gap between classical spatial geometry and next-generation AI. You will lead the design and development of robust state estimation, long-term mapping, and localisation loops, with a specific focus on integrating modern machine learning techniques and foundation geometry models into our core navigation stack. Your work will ensure our robots possess rock-solid spatial awareness and execute highly reliable trajectories over extended deployments in complex, real-world environments.

What You'll Do

  • Design and scale robust SLAM pipelines that maintain highly accurate localisation and state estimation across dynamic physical spaces.

  • Integrate foundation geometry models and modern ML techniques into the localisation loop to enhance traditional spatial tracking, data association, and visual odometry.

  • Architect and maintain robust long-term mapping systems that enable robots to autonomously update, manage, and scale spatial maps across changing environments over time.

  • Build and scale auto-labeling data pipelines that leverage foundation geometry models to generate high-fidelity spatial ground truth for downstream ML training.

  • Bridge classical geometry with deep learning architectures, building hybrid systems that map raw sensor data into globally consistent metric trajectories.

  • Deploy and benchmark spatial AI software directly on physical robot hardware, ensuring deterministic, ultra-low latency execution on edge compute.

  • Translate cutting-edge spatial AI research into production reality, continuously evaluating breakthroughs in geometric learning to guide our navigation roadmap.

What We're Looking For

  • Deep expertise in classical SLAM and multi-view geometry, including extensive hands-on experience with state estimation, visual/LiDAR odometry, and multi-sensor fusion.

  • Experience handling backend map optimization and solving the unique structural challenges associated with long-term mapping in large or changing physical environments.

  • Practical experience applying modern ML to geometric problems, with exposure to utilizing or adapting newer foundation geometry models.

  • Proven ability to build scalable data pipelines or auto-labeling workflows tailored for geometric and spatial datasets.

  • Proficiency in C++ and Python, with a track record of writing clean, highly parallelized, production-grade algorithmic code.

  • Hands-on experience deploying navigation systems on physical hardware, with a deep understanding of the practical edge constraints of real-world robotics.

  • A research-to-production mindset, capable of tearing through the latest CVPR/ICRA papers and figuring out exactly how to make those models run with bulletproof reliability.

What We Offer

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

  • 30+ paid days off, including 23 days of annual leave, all UK bank holidays, and additional company closure days (including Christmas–New Year shutdown).

  • Private healthcare, including virtual and in-person care.

  • Pension scheme with 8% total contribution (5% employee, 3% employer) on full earnings.

  • Free daily breakfast, catered lunch, and snacks in-office.

  • Work at the frontier - collaborate daily with world-class engineers, researchers, and product experts building the next generation of AI and humanoid robotics.

  • Real ownership - direct access to founding leadership, meaningful input on product direction, and the ability to drive key initiatives from day one.

Posted 2026-07-18

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