Software Engineer Intern

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, re-cut a fixture, sit next to the cell and watch it fail 40 times. This is the job.

What we're looking for

Required

• 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.

Nice to have (genuinely optional — we don't expect all of these)

• Hands on real robots: ROS/ROS2, arm kinematics, cameras, calibration.
• Familiarity with modern manipulation policies — ACT, Diffusion Policy, VLAs (π0, RDT, OpenVLA, GR00T).
• CAD, fixturing, or any ability to make a physical thing exist.
• You've run an experiment that failed and can explain precisely why.

What we don't require

• Publications.
• Prior internships.
• Knowing the specific model families above. If you can learn fast and debug carefully, the domain is learnable in weeks.

How we work

• Small scope, real ownership. You'll own something that ships, not a side quest that gets archived in September.
• Evidence over vibes. We read papers, and we assume their numbers are optimistic until we reproduce them. Independent verification is a first-class activity here.
• Honest failure reporting. Telling us early that an approach isn't working is more valuable than making it look like it is.
• Physical reality wins. A load cell and an if-statement beat an elegant policy that can't sense the thing it needs to sense.