Robot Learning Engineer

knuspr.de; Berlin; On-site; EUR 70,000 - 100,000; Full time

Job summary

Knuspr.de is building the autonomous grocer and seeks a machine-learning lead to own the capture spec and initial training data for robot policies. You will define how we capture and label data, and ensure the data supports scalable policy learning in warehouse environments.

You will own ground-truth hand pose, handle measurement against real warehouse conditions, and build evaluation tools to track progress as the dataset grows.

Qualifications

• 3+ years of relevant full-time robotics experience.
• Proficiency in PyTorch and computer vision with real 3D geometry.
• Camera calibration, pose estimation, and SLAM fundamentals.
• Ability to read a paper and reproduce it.
• Knowledge of robot-learning stack and policy classes.
• Experience with data collection and failure modes in training.
• Ability to act as the sole ML voice and drive data quality decisions.
• Familiar with multi-camera setups and egocentric video in production.

Responsibilities

• Write the capture spec: cameras, mounts, calibration, disqualifiers and enforce it on the fleet.
• Define ground truth for hand pose and warehouse realities; measure and close gaps.
• Build the evaluation harness to determine bankable hours for training data.
• Lead the first training runs and post-train robot-learning models on the data.

Skills

Robotics

PyTorch

Computer vision

Camera calibration

Pose estimation

SLAM fundamentals

Paper reproduction

Policy training data

Tools

ROS 2

Job description

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, Knuspr.de, Kifli.hu, Gurkerl.at and Sezamo.ro, choosing from over 17,000 items, delivered within a couple of hours in 15-minute windows.

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, Knuspr.de, Kifli.hu, Gurkerl.at and 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 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 and 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 offer

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