Robot Learning Engineer

Overview

• 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.

WHY THIS ROLE IS EXCITING

Rohlik is the leading Central European e-grocer. More than a million customers buy their groceries from us through Rohlik.cz http://Rohlik.cz, Knuspr.de http://Knuspr.de, Kifli.hu http://Kifli.hu, Gurkerl.at http://Gurkerl.at and Sezamo.ro http://Sezamo.ro, choosing from over 17,000 items, delivered within a couple of hours in 15-minute windows.

We are building the autonomous grocer. Maia, our AI assistant, already talks to customers and builds their orders; agents are moving into our buying and logistics; vision models are learning to watch quality. The next frontier is physical: robots taking over more and more of the work in our fulfilment centres, until fresh food moves from producers to households at a cost nobody else can match.

We are setting out to record how our own people handle groceries and to turn those recordings into a training corpus for manipulation policies. Our warehouses already log what happened on every pick and every quality check, so the recordings can carry labels most datasets never get. Making that hold is part of the job.

You decide what makes an hour of footage trainable — before we bank hundreds of them.

WHAT YOU WILL OWN AND DELIVER

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

• 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, t