Senior Engineer, Vision‑Language‑Action (VLA) / Behaviour Learning

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.

Vision

In a world where artificial intelligence opens up new horizons, our faith in its potential unveils a new outlook where, together, humans and machines build a new future filled with knowledge, inspiration, and incredible discoveries. The development of a functional humanoid robot underpins an era of abundance and well-being where poverty will disappear, and people will be able to choose what they want to do. We believe that providing a universal basic income will eventually be a true evolution of our civilization.

Solution

As the demands on our built environment rise, labour shortages loom. With the world’s workforce increasingly moving away from undesirable tasks, the manufacturing, construction, and logistics industries critical to our daily lives are left exposed. By deploying our general-purpose humanoid robots in environments deemed hazardous or monotonous, we envision a future where human well-being is safeguarded while closing the gaps in critical global labour needs.

About the Role:

In this role, you will work on all aspects of training capable manipulation policies, be it pre-training of a base policy on a diverse multi-embodiment corpus of manipulation trajectories, fine-tuning models to perform a specific task well, curating data collection processes or exploring productive ways to use synthetic data. This is primarily a deep learning-focused role, so we are looking for experience solving real problems using modern neural networks, and experience in robotics isn’t strictly required. However if you don’t have such experience, be prepared that you’d need to familiarize yourself with a new domain quickly.

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.

How to Apply

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