Deep Learning Engineer in London

Humanoid is the first AI and robotics company in the UK, creating the world's most advanced, reliable, commercially scalable, and safe humanoid robots. Our first humanoid robot HMND 01 is a next-gen labour automation unit, providing highly efficient services across various use cases, starting with industrial applications.

Our Mission
At Humanoid we strive to create the world's leading, commercially scalable, safe, and advanced humanoid robots that seamlessly integrate into daily life and amplify human capacity.

What You'll Do:

• Train policies via representation learning, behaviour cloning and RL; own the full loop from data to deployment.
• Partner with teleoperations to drive data collection: specify what 'good' looks like, ensure diversity/coverage, and close the gap between sim and real.
• Run pre-/mid-/post-training on multimodal LLM/VLM/VLA stacks; plug in new modalities (vision, audio, proprioception, LiDAR/point clouds, ...) without breaking existing ones.
• Build and maintain continuous pipelines: ingest simulation + tele‐op logs, version them, apply weak‐supervision labelling, curate balanced datasets, and auto‐surface fresh failure cases into retraining.
• Work with MLOps & Data Platform teams to scale distributed training and optimize models for real‐time edge inference.

We’re Looking For:

• 3+ years building deep‐learning systems (industry or research) with shipped models or published artifacts to show for it.
• Hands‐on with at least one of: LLMs, VLMs, or image/video generative models — architecture, training, and inference.
• Experience with deep learning infrastructure: streaming datasets, checkpointing & state management, distributed training strategies.
• Strong Python + PyTorch/JAX; you can profile, debug numerics, and write maintainable research code.
• You document experiments clearly and communicate trade‐offs crisply.

Nice-to-Have:

• Robotics or autonomous driving experience.
• RL for LLMs or robotics (PPO, DPO, SAC, etc.).
• Proven productization of deep nets (latency/throughput constraints, telemetry, on‐device optimization).
• Publications at ICLR/ICML/NeurIPS or equivalent open‐source contributions.
• Familiarity with OpenVLA, Physical Intelligence (π) models, or similar open VLA frameworks.

What We Offer:

• Meaningful time off to rest and recharge: 23 days of annual leave (accrued), 15 days of paid sick leave, and paid 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.
• Equity included–we believe builders should share in what they build.
• 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.