humanoid - VLA Pre-training Engineer (Deep Learning)
Requirements
• 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.
Responsibilities
• Post-train policies via behaviour cloning and RL; own the full loop from data to deployment. • Partner with the Data Collection team to drive collecting new data: specify what good data looks like, identify failure modes, ensure diversity and coverage. • Work closely with external partners to ensure steady supply of high-quality pretraining-scale data. • Run pre-/mid-/post-training on VLA stack; explore new modalities and architecture changes. • 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. • Work with MLOps & Data Platform teams to scale distributed training and optimize models for real‑time edge inference.
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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