humanoid - Staff AI Engineer, Robot Learning (Navigation)
Requirements
• 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.
Responsibilities
• 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.
Benefits
• Meaningful time off to rest and recharge: 23 days of annual leave (accrued), separate sick leave, and paid bank holidays and 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. • 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.
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