VLA Pre-training Engineer - Deep Learning

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.

We're hiring a VLA Pre-training Engineer to join our Autonomy team based in London. In this role you will you will work on all aspects of training capable policies, be it pre-training of a base model on a diverse multi-embodiment corpus of trajectories, fine-tuning a policy to perform a specific task well, curating data collection processes or exploring productive ways to generate and use synthetic data. This is primarily a deep learning-focused role, so we are looking for experience solving real problems using modern neural networks, while 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.

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  Post-train policies via behaviour cloning and RL; own the full loop from data to deployment.

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  Partner with the Data Collection team to drive collecting new data: specify what good data looks like, identify failure modes, ensure diversity and coverage.

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  Work closely with external partners to ensure steady supply of high-quality pretraining-scale data.

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  Run pre-/mid-/post-training on VLA stack; explore new modalities and architecture changes.

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  Build and maintain continuous pipelines: ingest synthetic data and teleop logs, version them, apply weak‑supervision labelling, curate balanced datasets, and auto‑surface fresh failure cases into retraining.

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  Work with MLOps & Data Platform teams to scale distributed training and optimize models for real‑time edge inference.

• 3+ years building deep‑learning systems (industry or research) with shipped models or published artifacts to show for it.

• Deep hands‑on experience 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.

• Familiarity with modern software engineering practices.

• You document experiments clearly and communicate trade‑offs crisply.

• Robotics or autonomous driving experience.

• Experience applying RL to LLMs or robotics.

• Experience with VLA (vision-language-action) models.

• Proven productization of deep nets (latency/throughput constraints, telemetry, on‑device optimization).

• Publications at top-tier deep learning conferences or equivalent open‑source contributions.

• Familiarity with OpenVLA, Physical Intelligence (π) models, or similar open source VLA frameworks.