Robotics Learning Engineer — Manipulation & Teleoperation

Genesis AI; Paris; Sur place; EUR 70 000 - 110 000; Plein temps

Résumé du poste

Genesis AI in Paris is seeking a seasoned robotics engineer to develop and optimize a learning-based robotic manipulation control stack and teleoperation system with low latency.

You will train policies using reinforcement and imitation learning, deploy them on real robots, and work with a team to close the sim-to-real gap and improve end-to-end performance.

Strong ownership and a methodical approach are essential to tackle complex multi-disciplinary challenges in a fast-moving environment.

Qualifications

• Passion for robotics engineering and demonstrated excellence in the field.
• Exceptional ownership and initiative—finding and solving problems independently.
• Focus, attention to detail, patience, and a methodical approach to complex tasks.
• Production-level expertise in modern Python or C++.
• Extensive experience building and deploying real-world learning-based robotic systems (5+ years).
• Bonus: Hands-on experience troubleshooting and maintaining robotic systems across mechanical, electrical, and software components.

Responsabilités

• Develop and optimize a learning-based robotic manipulation control stack.
• Design and maintain a teleoperation system with smooth, precise motion and low latency.
• Train robotic policies for manipulation and locomotion with reinforcement learning and imitation learning.
• Deploy robotic policies and diagnose latency or bottlenecks in the control pipeline.
• Analyze and minimize the sim-to-real gap by co-optimizing simulation and real-world robot behavior.
• Collaborate with a team of driven individuals committed to building general-purpose Physical AI.

Connaissances

Python

C++

Robotics

Reinforcement learning

Imitation learning

Description du poste

Genesis AI in Paris is seeking a seasoned robotics engineer to develop and optimize a learning-based robotic manipulation control stack and teleoperation system with low latency.

You will train policies using reinforcement and imitation learning, deploy them on real robots, and work with a team to close the sim-to-real gap and improve end-to-end performance.

Strong ownership and a methodical approach are essential to tackle complex multi-disciplinary challenges in a fast-moving environment.