Senior data collection operator; FR

About Genesis

We are a full-stack general-purpose robot company, born across San Francisco, London and Paris and bound by a single mission: to make general-purpose robots a reality, unlocking infinite physical labor for the world and freeing people for creativity, exploration and the pursuits they love for their own sake.

We're building the engine that will teach robots to do real work in the real world. We recently unveiled Eno, our wheeled, dexterous robot powered by our GENE foundation model and we are starting to put it to work with real customers.

General-purpose robots learn from real, dexterous human work, the kind of data that does not exist on the internet. We capture it with our own collection hardware: a person wears it, does the work, and the robot learns to do it too.

You will be a senior member of the team scaling our data collection operation in Paris, working closely with our Paris research and engineering teams and with the operation in the Bay Area.

You will help lead the day-to-day execution and quality bar on the Paris floor while remaining deeply hands-on with data collection, teleoperation, task setup, and real-robot testing. You will wear our gloves and cameras, teleoperate our hardware, and take ownership of complex collection requests from setup through execution and quality review.

You will work shoulder to shoulder with our model engineering and research teams, turning their requests into physical demonstrations and feeding what you learn on the floor back to them. You will be expected to operate autonomously, help guide and train other operators and contribute to how the Paris operation evolves as the team grows.

This is a hands-on leadership role. You will not just run existing tasks: you will help shape how they are executed, how quality is measured, and how other operators are trained to deliver consistently.

• Help lead the Paris data collection floor. Set the execution and quality bar for day-to-day collection work and help the team scale effectively.

• Own complex collection setups. Take a loosely defined request and turn it into a working, repeatable collection task, including the setup, fixtures, props, environment, and procedure, with minimal support from engineering.

• Create high-quality training data. Wear our gloves and cameras, teleoperate our hardware, and produce precise demonstrations that can be used to train our models.

• Own the full collection workflow. Go beyond collecting the data itself: review and quality-check your work, identify unusable episodes, support annotation, and help ensure that what reaches research is trainable data.

• Build repeatable tasks. Turn new collection requests into clear, repeatable processes that can be executed consistently by other operators.

• Partner with research and model engineering. Translate their requests into physical collection tasks, share observations from the floor, and iterate with them on setups and collection strategy.

• Improve the operation. Identify when a fixture, station layout, procedure, or collection method can be improved and proactively help implement those improvements.

• Write and improve the playbook. Own SOPs, quality checks, and training materials that allow the Paris operation to scale while maintaining a consistent quality bar.

• Guide other operators. Help onboard, train, and mentor operators and reinforce consistent execution across the team.

• Run real-robot testing. Partner with reliability and engineering teams to test robots on the floor, document what you observe, and help surface issues.

• Own quality. Review your own work and the team's, take direct feedback, and keep raising the bar.

• You have done precise, repetitive physical work to a high quality bar and can explain what made your work good and how you measured it. This could include data collection, teleoperation, motion capture, robotics lab work, assembly, fine craft, or another hands-on technical environment.

• Hands-on robot or glove-based data collection experience is a strong plus, but not a requirement. This kind of data collection is new, and we will teach you our specific tools and approach.

• You build, not just run. Give you a vague request and a scrappy setup, and you can turn it into a clean, repeatable task with the right fixtures, environment, process, and written procedure.

• You can operate autonomously. You are comfortable taking ownership of complex collection tasks with limited day-to-day support from engineering.

• You…