Senior Autonomy Engineer (SLAM & Navigation)

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.

Responsibilities

• 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.

Requirements

• 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.

Benefits

Additional Information

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.

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