Backed by Khosla.
About the role
Simulation is still a hard sell in robotics. Outside a few success stories, like reinforcement learning for locomotion, most teams skip it, and friction is a big part of why: painful to use, painful to debug. We live that pain ourselves: debugging a failing experiment during policy evaluation takes 30 minutes, and there is no way to automatically generate a standalone reproduction script. That blocks adoption, even internally.
What they're looking for
- You care about the engineer using what you build as much as the thing itself. A slow test, a cryptic error, a manual step: you see it, you go after the root cause, and you remove it for everyone. You have opinions about API design
- You are comfortable with physics and robotics: the right design choices take a working picture of what a simulator like Genesis-World does, how it works, and how it is used, a picture you can partly build on the job
- Strong Python engineering on large codebases: architecture, refactoring, typing, packaging, performance profiling (py-spy, Nsight, Xcode Instruments, RenderDoc)
- Developer tooling, test infrastructure, or CI at scale
- Enough fluency with JIT compilation, code generation, or GPU computing stacks to reason about compilation caching, kernel dispatch, and startup cost. You will work closely with Quadrants
- At ease debugging across the full platform matrix: Windows, Linux, and macOS, on x86 and arm64, over CUDA, AMD, and Apple Metal (and possibly Intel XPU via Vulkan soon)
More about this role
Robots will learn in simulation before they hit the factory. Genesis-World is our bet on that future.
Genesis-World is an open-source, general-purpose simulation platform for physical AI from Genesis AI . One unified multi-physics engine: rigid bodies, FEM, MPM, particles, cloth, fluids. A robot arm can pour water onto sand, grasp a deformable object, or cut a soft body, all in the same simulation. Nyx, our in-house renderer, may be the most promising renderer for robotics out there: real-time photo-realistic rendering, advanced features like depth of field, and state-of-the-art techniques never seen before. Sensors of every kind: cameras, lidar, IMU, contact forces, temperature, plus arguably the most advanced tactile simulation available ( paper ). And the engine keeps growing: we are developing internally the most comprehensive and fastest Incremental Potential Contact ( paper ) solver for deformable body dynamics we know of, soon to be open-sourced. It powers real business applications, from full-fledged box packaging with labelling machine and all, to wire harnessing and lab automation, without any physics hack or compromise.
Everything is Python-first and runs anywhere....
Browse similar: AI jobs · AI startup jobs · Startup jobs · San Francisco Bay Area