Solution

As the demands on our built environment rise, labour shortages loom. With the world’s workforce increasingly moving away from undesirable tasks, the manufacturing, construction, and logistics industries critical to our daily lives are left exposed. By deploying our general-purpose humanoid robots in environments deemed hazardous or monotonous, we envision a future where human well-being is safeguarded while closing the gaps in critical global labour needs.

What You'll Do:

• Train policies via representation learning, behaviour cloning and RL; own the full loop from data to deployment.
• Partner with teleoperations to drive data collection: specify what “good” looks like, ensure diversity/coverage, and close the gap between sim and real.
• Run pre-/mid-/post-training on multimodal LLM/VLM/VLA stacks; plug in new modalities (vision, audio, proprioception, LiDAR/point clouds, …) without breaking existing ones.
• Build and maintain continuous pipelines: ingest simulation + tele‑op 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.

We’re Looking For:

• 3+ years building deep‑learning systems (industry or research) with shipped models or published artifacts to show for it.
• Hands‑on 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.
• You document experiments clearly and communicate trade‑offs crisply.

Nice-to-Have:

• Robotics or autonomous driving experience.
• RL for LLMs or robotics (PPO, DPO, SAC, etc.).
• Proven productization of deep nets (latency/throughput constraints, telemetry, on‑device optimization).
• Publications at ICLR/ICML/NeurIPS or equivalent open‑source contributions.
• Familiarity with OpenVLA, Physical Intelligence (π) models, or similar open VLA frameworks.

What We Offer:

• Competitive salary plus participation in our Stock Option Plan
• UK Private Insurance
• Paid vacation with adjustments based on your location to comply with local labor laws
• Travel opportunities to our Vancouver and Boston offices
• Office perks: free breakfasts, lunches, snacks, and regular team events
• Freedom to influence the product and own key initiatives
• Collaboration with top‑tier engineers, researchers, and product experts in AI and robotics
• Startup culture prioritising speed, transparency, and minimal bureaucracy