VLA Pre-training Lead (Deep Learning) in London

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 Alpha - our rapidly developed humanoid platform now running in real industrial pilots - and we’re growing the team to take it even further.

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.

About the Role

As Pretraining VLA Lead, you will own the pretraining stage of our VLM and VLA-based framework powering the fleet across wheeled and bipedal platforms. You will define the base model strategy — architecture, data mixture, and scaling — and lead a team of research engineers training foundation policies on a diverse, multi-embodiment corpus of real-world trajectories, teleoperation and synthetic data, and internet-scale video and language data. The base models you deliver are the substrate every downstream team fine-tunes for locomanipulation, so your decisions shape the capability ceiling of every robot we ship.

What You'll Do

• Own the VLA pretraining roadmap end-to-end: architecture choices, data mixtures, scaling laws, and evaluation protocols for base models.
• Push pretraining beyond a single recipe: explore transformer- and diffusion-based architectures, video pretraining, and world-model objectives that turn multimodal data (video, action, state, language) into generalisable robot capabilities.
• Lead, grow, and mentor a team of deep learning engineers focused on pretraining, setting research direction and engineering standards.
• Design and run large-scale distributed training on multi-node GPU clusters; drive throughput, stability, and cost efficiency in partnership with MLOps & Data Platform teams.
• Define what pretraining-scale data looks like: partner with the Data Collection team and external data providers to secure a steady supply of high-quality, diverse, multi-embodiment trajectories.
• Build rigorous base-model evaluation suites that predict downstream post-training and real-robot performance, and use them to make principled go/no-go scaling decisions.
• Establish continuous pretraining pipelines: dataset versioning, curation, deduplication, weak-supervision labelling, and automatic surfacing of coverage gaps.
• Collaborate with post-training and RL teams to ensure base models transfer cleanly to fine-tuning and real-time edge inference.
• Track and drive the frontier: evaluate emerging VLA architectures, modalities, and training recipes, and decide what enters our production stack.

What We're Looking For

• A track record of building deep-learning systems (industry or research), with shipped models or published artifacts to show for it, and experience leading a team or a major workstream.
• Proven experience pretraining large models — LLMs, VLMs, video/generative models, or VLAs — at multi-node scale: you have owned data mixtures, scaling decisions, and training stability for large distributed runs.
• Deep understanding of transformer and diffusion architectures, multimodal training, and the practicalities of distributed training.
• Strong Python + PyTorch/JAX; you can debug and profile ML systems and write maintainable research code.
• A track record of making data-driven scaling decisions and communicating trade-offs crisply to both researchers and leadership.
• You document experiments clearly and build teams that do the same.

Nice to have

• Experience with VLA (vision-language-action) models and frameworks.
• Robotics or autonomous driving experience, especially multi-embodiment or cross-platform learning.
• Experience with synthetic data generation and sim-to-real pipelines at scale.
• Publications at top-tier deep learning conferences (NeurIPS, ICML, ICLR, CoRL) or equivalent open-source contributions.
• Experience optimising foundation models for real-time edge inference.

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.