What You’ll Do
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
What You’ll Bring
Passion for your craft and demonstrated excellence in robotics engineering
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
Skills Required
- Demonstrated excellence in robotics engineering
- Production-level expertise in modern Python or C++
- Extensive experience building and deploying real-world learning-based robotic systems (5+ years)
- Experience training robotic policies with reinforcement learning and imitation learning
- Experience designing and maintaining teleoperation systems and robotic control stacks
- Hands-on experience troubleshooting and maintaining robotic systems across mechanical, electrical, and software components
What We Do
Genesis AI is a global full-stack robotics company developing general-purpose robots with human-level intelligence and capabilities. It aims to build foundational AI models that automate repetitive tasks across applications such as lab work and housekeeping. The company uses a proprietary physics engine to generate synthetic physical-world data, helping train robotics models for diverse real-world environments, and operates across Paris and Silicon Valley.
