Robotics Learning Engineer: Teleoperation & Policy Training

Genesis AI; Paris; On-site; EUR 90,000 - 130,000; Full time

Job summary

Genesis AI in Paris is seeking a robotics engineer to develop and optimize a learning-based manipulation control stack and to design a teleoperation system with low latency. You will train policies for manipulation and locomotion using reinforcement and imitation learning, deploy policies, diagnose bottlenecks, and reduce the sim-to-real gap while collaborating on general-purpose Physical AI.

The role emphasizes ownership, detail, and independent problem solving, with production-level Python/C++

Qualifications

• Passion for robotics engineering and demonstrated excellence.
• Strong ownership and initiative to solve problems independently.
• Focus, attention to detail, patience, and a methodical approach to complex tasks.
• Production-level expertise in Python or C++.
• 5+ years of experience building and deploying learning-based robotic systems.
• Bonus: Troubleshooting across mechanical, electrical, and software components.

Responsibilities

• 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 to build general-purpose Physical AI

Skills

Robotics engineering

Ownership & initiative

Attention to detail

Python

C++

Reinforcement learning

Job description

Genesis AI in Paris is seeking a robotics engineer to develop and optimize a learning-based manipulation control stack and to design a teleoperation system with low latency. You will train policies for manipulation and locomotion using reinforcement and imitation learning, deploy policies, diagnose bottlenecks, and reduce the sim-to-real gap while collaborating on general-purpose Physical AI.

The role emphasizes ownership, detail, and independent problem solving, with production-level Python/C++