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.