The company develops humanoid robots for real industrial environments - manufacturing, logistics, warehouses, and large-scale physical operations.
What makes the company especially interesting is that its work has already moved well beyond "research-only robotics." The company's humanoid platform is already running in real industrial pilots, it has roughly 30,000 global preorders, and its partners include major industrial and technology corporations. The company also collaborates with leading players in advanced computing and consumer electronics.
This role sits within the VLA (Vision-Language-Action) team - the group responsible for robot manipulation, autonomy, reasoning, and sim-to-real learning systems. In practice, the team builds the core intelligence behind how humanoid robots understand and interact with the physical world.
Why this is interesting:
Working on real robots operating in real-world environments - not purely simulation research.
Opportunity to join one of the few teams globally building humanoid robotics at commercial scale.
Fast-growing environment with high ownership and direct impact on core systems.
Strong technical environment across RL, VLMs, LLMs, simulation, manipulation, and robotics infrastructure.
What You Will Do:
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.
What We're Looking For:
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.
Nice to have:
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.
What the Company Offers:
Competitive compensation package depending on level, experience, and background.
This role is fully onsite in London. Robotics engineers work directly with physical robot prototypes and lab infrastructure on a daily basis, so the company currently does not support remote or hybrid work for these teams.
Meaningful time off to rest and recharge: 23 days of annual leave (accrued), 15 days of paid sick leave, and paid company holidays.
Fully funded private healthcare
Equity included - we believe builders should share in what they build.
Pension scheme with a total 8% contribution (5% employee, 3% employer) on full earnings.
Free daily breakfast, catered lunch, and snacks in‑office.
Collaboration with top‑tier engineers, researchers, and product experts in AI and robotics.
Freedom to influence the product and own key initiatives.