Genesis-World: Core Simulation Engine Engineer

Job description

What we’re building 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. Kernels are written once, and Quadrants, our in-house JIT compiler, lowers them to CUDA, AMD ROCm, Apple Metal, Vulkan, x86, and ARM64. A single laptop or a datacenter. Massively batched GPU simulation for learning at scale, and complex non-batched scenes where CPU wins outright. This is at the core of Genesis AI’s strategy . Evaluation is the bottleneck of scalable robotics: real hardware caps iteration at wall-clock time, but simulation turns it into a compute problem. Ours already runs two orders of magnitude faster than hardware (tens of thousands of episodes in half an hour instead of 200+ hours), while correlating with on-hardware rollouts at 89%. The north star: physical AI that improves at the speed of compute. 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. Your job is to…