Humanoid
VLA Pre-training Lead (Deep Learning)
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About Humanoid
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.
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.
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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.
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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.
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