Head of Robot Learning

**San Francisco, CA or Hong Kong / Taipei (on-site preferred) | Full-time** **You'll be Anvil's first robot learning hire — the person who takes the papers everyone is retweeting, gets them running on our hardware in weeks, and ships them as demos and guides polished enough that the whole field notices.** Anvil is building the platform layer for Physical AI — robotics hardware and software that's radically more accessible than legacy industrial solutions. In our first 12 months we built and shipped 200+ robots (OpenARM and OpenYAM manipulators, Linux Devboxes, teleop kits, and a UMI-style handheld data-collection device) to customers in 60+ countries, doing over $2.1M in revenue on our own Taipei manufacturing line. And here's what makes this seat unusual: **not how much data we have — how much leverage you'd have over what gets collected.** Most robot learning engineers work with whatever data someone else decided to collect, on rigs they can't change. Here, the entire collection system bends to your judgment — and there's a factory behind it. We design and build our own UMI-style handheld collector in-house, on our own Taipei manufacturing line, sitting inside the Asia supply chain: if the data would be better with a different camera, a different mount, a new sync scheme, or a custom fixture that doesn't exist yet, you prescribe it and it gets built — in weeks, not procurement quarters. We have factory access most teams can't get — our own facility and our investors' and partners' plants — meaning differentiated, real-industrial-task data rather than the same recycled public datasets. And we have people who can do the collection work if you write the protocol: you prescribe what good data looks like, they collect it. Let's be honest about scale, because it matters: this is not a foundation-model data operation, and we're not pretending it is. It's the setup to reach LeRobot-shirt-folding scale — hundreds of high-quality demonstrations of the right task, on tooling shaped to your spec — with more control and less friction than almost anyone in the field gets. **The situation you're walking into:** - We have 200+ robots in the field and zero dedicated ML function. Model training happens in the gaps between the founders' and controls engineers' actual jobs. You are hire #1 for the entire robot learning function, and for the foreseeable future the team is you. - We ship a UMI-style handheld data-collection device — and nobody has yet closed the loop of training a policy purely from its data and running it on our arms. Validating that pipeline end to end is a product decision waiting on you, and it's one of your first deliverables. - Robot learning is compounding weekly — VLAs, diffusion policies, ACT, world models. At our stage the highest-leverage move is not novel research; it's replicating the best public work on our hardware fast, and publishing it. Think the LeRobot shirt-folding project — data to deployment, in the open — running on Anvil arms, with our name on the guide. - Demos are not vanity here. A flagship replication is simultaneously marketing to the exact community that buys us and the enablement guide our customers follow. Which means the last 10% — the clean repo, the honest success rates, the written guide, the good video — is where most of the value lives. We need a builder with a real knack for polish before calling a project done. - The collection system above is built but undirected. The UMI exists and can be revised to your spec, the operators exist, the factory access exists — but nobody with ML judgment decides what to collect, how, and what "good" looks like. The leverage is sitting there; the person who prescribes it doesn't exist yet. **What you'll own:** - **Training pipelines, end to end:** teleop and UMI data ingestion, dataset formats and quality triage, training jobs, and an eval harness with honest success-rate protocols — built so that eventually someone who isn't you can train a model. - **Pap…