deploying the first billion robots for manufacturing. Backed by Y Combinator.
About the role
You'll work on the stack that gets a policy from "recorded a human doing it" to "running on a line." Depending on your strengths and what's on fire, that could mean: Policy work. Imitation learning (ACT, diffusion policies), visual servoing, observation-space design. Our experience matches the published literature here: what you condition the policy on matters more than which backbone you pick.
What they're looking for
- Strong Python. You've written code other people had to maintain
- Comfort with PyTorch — you can read a training loop and know where to put a breakpoint
- Enough Linux and git to be self-sufficient
- Empirical instincts. When something doesn't work, your first move is to isolate a variable, not to change three things and re-run
- Currently enrolled in CS, EE, ME, robotics, or equivalent — or you can demonstrate the skills some other way. We care about the second clause
- Hands on real robots: ROS/ROS2, arm kinematics, cameras, calibration
More about this role
You'll work on the stack that gets a policy from "recorded a human doing it" to "running on a line." Depending on your strengths and what's on fire, that could mean:
Data collection tooling. Teleop capture, episode management, labeling pipelines. The difference between 8 minutes of usable demonstrations and 8 minutes of garbage is almost entirely tooling.
Training and evaluation infrastructure. Reproducible runs, checkpoint management, and — the part everyone skips — real evaluation harnesses. If we can't measure a policy's success rate with enough trials to trust the number, we don't know anything.
Policy work. Imitation learning (ACT, diffusion policies), visual servoing, observation-space design. Our experience matches the published literature here: what you condition the policy on matters more than which backbone you pick.
Perception glue. Segmentation and tracking models feeding structured observations to controllers. Lots of SAM2, DINOv2, and small task-specific networks.
Deployment. Getting the above onto real hardware, at real control frequencies, without it falling over on hour four.
You will also do unglamorous things: fix the data loader, chase a 20 Hz timing mismatch,...
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