Asks for

The ad says“Extensive experience in machine learning for embodied AI, with a proven track record explicitly focused on end-to-end (e2e) learned behaviors using large models (VLAs, VLMs, transformers, or diffusion).”
The ad says“Extensive experience in machine learning for embodied AI, with a proven track record explicitly focused on end-to-end (e2e) learned behaviors using large models (VLAs, VLMs, transformers, or diffusion).”
The ad says“Extensive experience in machine learning for embodied AI, with a proven track record explicitly focused on end-to-end (e2e) learned behaviors using large models (VLAs, VLMs, transformers, or diffusion).”
The ad says“Extensive experience in machine learning for embodied AI, with a proven track record explicitly focused on end-to-end (e2e) learned behaviors using large models (VLAs, VLMs, transformers, or diffusion).”
The ad says“Strong background in spatial reasoning and semantic goals, with experience handling multi-agent dynamics, crowding, or interactive environments.”
The ad says“Proficiency in PyTorch and the modern tooling required to train, fine-tune, and deploy large-scale foundation models for robotics.”
The ad says“Strong background in spatial reasoning and semantic goals, with experience handling multi-agent dynamics, crowding, or interactive environments.”
The ad says“Strong background in spatial reasoning and semantic goals, with experience handling multi-agent dynamics, crowding, or interactive environments.”
The ad says“Extensive experience in machine learning for embodied AI, with a proven track record explicitly focused on end-to-end (e2e) learned behaviors using large models (VLAs, VLMs, transformers, or diffusion).”
The ad says“Extensive experience in machine learning for embodied AI, with a proven track record explicitly focused on end-to-end (e2e) learned behaviors using large models (VLAs, VLMs, transformers, or diffusion).”
The ad says“Extensive experience in machine learning for embodied AI, with a proven track record explicitly focused on end-to-end (e2e) learned behaviors using large models (VLAs, VLMs, transformers, or diffusion).”

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.

About the Role

We're hiring a Staff AI Engineer, Robot Learning (Navigation) to join our Perception and Navigation team based in London. In this role you will lead the design, development, and optimisation of next-gen robot learning systems for humanoid navigation, behavior learning, multi-agent interaction, and semantic goal reasoning in dynamic environments. We are interested in candidates who have a track record of driving end-to-end learned behaviours into production (e.g. self-driving, drones, robot navigation and other autonomous systems).

At the Staff level, you aren’t just implementing existing models; you are defining the paradigm for how humanoids interact with a dynamic, unpredictable world. You will own the stack that transitions our robots from structured laboratory tasks to fluid, real-world autonomy.

What You'll Do

Develop next-generation learned navigation systems that integrate complex spatial reasoning and semantic goals to drive robust, real-world robot behaviors.
Work on open-ended navigation powered by Vision-Language-Action (VLA) models, enabling robots to understand context, navigate multi-agent environments, predict intent, and act safely in dynamic spaces.
Design and scale data pipelines and evaluation frameworks optimized for training large-scale, end-to-end (e2e) learned behaviors and multimodal navigation models.
Architect and deploy highly reliable ML systems, taking models out of simulation/labs and hardening them for predictable, repeatable execution on physical hardware.
Collaborate with cross-functional research and engineering teams to productionize large vision-language-action models, ensuring production metrics meet strict real-world reliability standards.
Stay ahead of the field, rapidly evaluate new model architectures, multi-agent strategies, and datasets to guide our embodied AI and behavior-learning roadmap.

What We're Looking For

Extensive experience in machine learning for embodied AI, with a proven track record explicitly focused on end-to-end (e2e) learned behaviors using large models (VLAs, VLMs, transformers, or diffusion).
Deep production expertise: You are someone who gets things into production that work reliably. You have hands-on experience deploying, monitoring, and optimizing large-scale ML systems.
Strong background in spatial reasoning and semantic goals, with experience handling multi-agent dynamics, crowding, or interactive environments.
Proficiency in PyTorch and the modern tooling required to train, fine-tune, and deploy large-scale foundation models for robotics.
Exceptional experimental and engineering skills, capable of taking ambitious behavior-learning concepts from initial research to rock-solid deployment on physical robots.
Comfortable working in a fast-moving, research-driven environment with evolving models, data, and tools.

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.