As the machine-learning voice on a small, newly created team you build a data corpus on which robot manipulation policies can be trained, and then train the first policies. The data comes from recordings of warehouse workers handling groceries.
Stack
Responsibilities
- ▹Write and enforce the capture spec: cameras, mounts, calibration and what disqualifies an episode
- ▹Own ground truth: measure hand pose and close the gap in warehouse conditions (work gloves, occlusion, cold halls)
- ▹Build the evaluation harness that decides which hours of footage make the corpus
- ▹Post-train open robot-learning models on the company's data and run the first task evaluations
- ▹Follow the literature, reproduce the claims the project depends on and steer capture before money is spent
Requirements
- ▹PyTorch and computer vision with real 3D geometry: camera calibration, pose estimation, SLAM fundamentals
- ▹Ability to read and reproduce papers
- ▹Familiarity with the current robot-learning stack and policy classes
- ▹Experience training on self-collected data and knowledge of training-time failure modes
- ▹Comfortable as the only ML voice: make the call, write it down and revisit it
- ▹Directing agents (Claude Code, Devin) for routine work while keeping the quality bar
Nice to have
- ▹Hand-pose estimation or egocentric video in production
- ▹Multi-camera rigs and time sync
- ▹Experience post-training robot-learning models
- ▹ROS 2
Soft skills
Decisiveness with incomplete informationDocumenting and revisiting decisionsIndependence
What we offer
- ▹The physical-AI side of the autonomous grocer, with the capture spec and first training runs yours from day one
- ▹A data asset few teams in the world get to build, in fresh groceries
- ▹Off-the-shelf hardware, a managed cloud estate and a mandate that has already started
About the company
Rohlik is the leading Central European e-grocer (Rohlik.cz, Knuspr.de, Kifli.hu, Gurkerl.at, Sezamo.ro) with over a million customers and 17,000 items.
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