We are a full-stack general-purpose robot company, born across San Francisco 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 are French and American by birth and global by nature. We are backed by partners who share our vision, including Eclipse, Khosla Ventures, Bpifrance, and HSG, alongside Eric Schmidt, Xavier Niel, Daniela Rus, and Vladlen Koltun. Our people work across the Bay Area and Europe, 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.
What You'll DoOwn the technical path from customer use case to working deployment, turning real-world workflows into solutions our robots can reliably execute.
Work directly with customers to understand their operations, assess automation feasibility, and define requirements, success metrics, and deployment scope.
Design and implement deployment solutions across product management, robot hardware, model capabilities, tooling, and customer systems, making practical tradeoffs between performance, reliability, and integration complexity.
Get hands-on at customer sites: configure systems, run evaluations, debug failures, and drive iterations until the solution meets agreed acceptance criteria.
Work closely with research, robotics engineering, and platform teams to turn field failures into actionable improvements, distinguishing what requires a better model, a software fix, or a change to the physical setup.
Build reusable tools, integration patterns, and deployment playbooks that make each new site faster and easier to bring online.
Track record of delivering technical solutions in real-world environments, with ownership that extends through integration, troubleshooting, and customer acceptance.
Strong hands-on engineering skills, including Python, comfort working in Linux, and experience integrating hardware and software through APIs, interfaces, or control systems.
Enough robotics depth to investigate a failed task across system.
Ability to translate an ambiguous customer request into a concrete technical plan, explain tradeoffs clearly, and set expectations grounded in what the system can deliver.
Strong bias toward the field over the whiteboard, with the persistence to work through the gap between a successful demo and reliable daily operation.
Clear written communication skills to document configurations, failure modes, and operating procedures that others can use without you in the room.
Comfort operating with incomplete information and traveling to customer sites to support deployments.
Bonus: Experience with robot manipulation, ROS/ROS 2, industrial automation, or integrating systems into manufacturing and logistics workflows.
Skills Required
- Track record delivering technical solutions in real-world environments through integration, troubleshooting, and customer acceptance
- Strong hands-on engineering skills with Python
- Comfort working in Linux
- Experience integrating hardware and software through APIs, interfaces, or control systems
- Sufficient robotics knowledge to investigate failed tasks across the system
- Ability to translate ambiguous customer requests into concrete technical plans and explain tradeoffs
- Strong written communication skills for documenting configurations, failure modes, and operating procedures
- Willingness to travel to customer sites and support deployments
- Experience with robot manipulation, ROS or ROS 2, industrial automation, or manufacturing and logistics system integration
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.
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