humanoid - VLA Pre-training Lead (Deep Learning)
Requirements
• 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. • 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.
Responsibilities
• 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.
Benefits
• 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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