Robot Learning Engineer

• The capture spec. Cameras, mounts, calibration, and what disqualifies an episode. You write it before we buy the fleet and enforce it after. When the spec and the floor disagree, you go to the floor and find out which one is wrong.
• Ground truth. Hand pose is the signal that transfers from human video to robot policies, and warehouse reality — work gloves, occlusion, cold halls — is exactly where the published models struggle. You own how we measure that and how we close the gap.
• The evaluation harness. The corpus only counts if a policy can train on it. You build the harness that decides which hours make the bank, and you hold the trainability bar as the volume grows.
• The first training runs. Once hours bank, you post-train open robot-learning models on our data and run the first task evaluations.
• Judgment before spend. The field moves monthly. You read what is published, reproduce the claims we depend on, and steer our capture before the money is spent, not after.

THE SCOPE

This is a seat on a small, newly created team: an operations lead who runs capture on the floor, a data engineer who owns the pipeline, and you: the machine-learning voice in the room. This is not a research-scientist role, and no publication record is required. The job is to make a corpus trainable and to train the first policies on it.

WHAT WE ARE LOOKING FOR

• 3+ years of relevant full-time robotics experience.
• PyTorch, and computer vision with real 3D geometry. Camera calibration, pose estimation, SLAM fundamentals. You have debugged an extrinsic calibration at least once in your life and know why it drifted.
• You can read a paper and reproduce it. Our pipeline is assembled from published work, and your job is knowing which claims survive contact with a chilled warehouse hall.
• You know what policies train on. Familiar with the current robot-learning stack and policy classes well enough to know what the training side will demand of the data before the data exists.
• Dataset instincts. You have trained on data you collected yourself, and you know the failure modes that only show up at training tim..
• Comfortable as the only ML voice. You make the call, write it down, and revisit it when the pilot data says otherwise.
• AI is how work gets done here. Our engineers use Claude Code and Devin daily, and we expect you to direct agents for the unglamorous parts — harness code, data plumbing, reproduction scripts — while keeping the quality bar exactly where it was.
• Particularly relevant. Hand-pose estimation or egocentric video in production; multi-camera rigs and time sync; experience post-training robot-learning models; ROS 2.

WHAT SUCCESS LOOKS LIKE IN 90 DAYS

You have written the capture spec and we set up the first rigs against it. Your pilot gates have numbers, the first hours have passed your harness, and you can tell the people who sign the budget, with data, what the next hundred hours should look like.

ARE WE A GOOD FIT?

Take this role if you would rather own the spec that decides whether a dataset becomes an asset or a write-off than tune someone else's model on someone else's data, if a fulfilment centre at 6 a.m. sounds like a lab to you, and if you want the shortest possible line between your technical judgment and a capital decision. Read our culture code https://career.rohlik.group/group/our-culture-code and are we a good fit? https://career.rohlik.group/group/are-we-a-good-fit.

WHAT WE OFFER

• The physical-AI side of the autonomous grocer, with the capture spec and the first training runs yours from day one
• A data asset few teams in the world get to build, in fresh groceries, a domain as hard as manipulation gets
• Off-the-shelf hardware, a managed cloud estate, and a mandate that has already started
• Benefits — see what we https://career.rohlik.group/group/what-we-offeroffer https://career.rohlik.group/knuspr/unser-angebot

ARE YOU IN?

Instead of a cover letter: pick a published human-video-to-robot-policy pipeline you rate, and tell us where it breaks on a warehouse floor where every hand wears a work glove.

#LI-ML1 #Knuspr

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Quelle: Statistisches Bundesamt (Destatis), Tabelle; dl-de/by-2-0; eigene Berechnung

Veröffentlicht am 01. Nov. 2026

Veröffentlicht am 01. Nov. 2026