Genesis-World: Core Physics Engineer in London

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. The engine keeps growing: we are developing internally the most comprehensive and fastest Incremental Potential Contact 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 datacentre. 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, while correlating with on-hardware rollouts at 89%. The north star: physical AI that improves at the speed of compute.

The role: You push the physics of Genesis-World forward. The mandate is clear: ship production-ready simulation capabilities that matter for the company's internal needs. Research applied end-to-end, from algorithm to merged, tested, documented code that real robot-learning pipelines depend on. Occasional groundbreaking research happens, notably through academic collaborations. But the core of the job is making the engine measurably better along five axes:

• Speed: Algorithms that are not only faster but also smart enough to spend compute only where it matters across both time and space.
• Completeness: No physics off limits: whatever the next use-case needs, the engine grows to cover it.
• Fidelity: More realistic models: contact, friction, deformation, energy, actuation, materials.
• Versatility: Extensible multi-physics without compromise on realism.
• Scalability: From workstation to factory scale, and one day, city scale.

Our ambition is to establish Genesis-World as the go-to simulator for physical AI, from companies and research labs to individuals.

The problems waiting for you: Every fidelity for every physics. Invent physics level-of-detail (LOD). Heterogeneous environments. Adaptive timesteps per island. Couple everything, exactly. More scalable constraint solvers. Unify contact resolution. Closed kinematic loops without constraints. Day to day: you write your physics in plain Python and Quadrants makes it fast on every backend. And you validate it the hard way: analytical closed forms, other engines, real-world data.

Who you are: You are a physicist and an engineer at once. You judge a method by whether it holds up in production at real scale, and you do not stop until it does. A strong background in physics-based simulation, preferably related to robotics. A track record of shipping simulation code that others rely on. Solid HPC programming (CPU and/or GPU), and an instinct for what makes a numerical method fast in practice. Rigor in validation: analytical closed forms, cross-engine consistency, real-world data. Bonus points: publications in simulation, graphics, or robotics venues. Contributions to an open-source physics engine.