{"slug": "genesis-world-core-physics-engineer-genesis-ai", "title": "Genesis-World: Core Physics Engineer — Genesis AI", "summary": "Genesis AI is hiring a Core Physics Engineer for its open-source simulation platform Genesis-World, which runs simulations two orders of magnitude faster than real hardware and correlates with on-hardware rollouts at 89%. The role focuses on advancing the engine's speed, completeness, fidelity, versatility, and scalability to establish it as the go-to simulator for physical AI.", "body_md": "# Genesis-World: Core Physics Engineer\n\n- Salary\n- Not published\n- Location\n- Bay Area\n- Work type\n- On-site\n- Posted\n- today\n\n[\nApply on company site (opens in new tab) ](https://jobs.ashbyhq.com/genesis/a99e725f-fa13-4f41-9ffc-3ad6305eb4e1/application)\n\nWhat we're building\n\nRobots will learn in simulation before they hit the factory. Genesis-World is our bet on that future.\n\nGenesis-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.\n\nEverything 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.\n\nThis 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.\n\nThe role\n\nYou 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:\n\n- Speed. Algorithms that are not only faster but also smart enough to spend compute only where it matters across both time and space: larger stable timesteps, selective fidelity (adaptive across scales or simply hand-set), structure-aware solvers.\n- Completeness. No physics off limits: water, human animation, air flow, gravel, tendons, even body organs. Whatever the next use-case needs, the engine grows to cover it.\n- Fidelity. More realistic models: contact, friction, deformation, energy, actuation, materials…\n- Versatility. Extensible multi-physics without compromise on realism: all solvers in the scene coupled together at once, two-way and constraint-based. Write your own solver and it joins the scene like a native one, growing into an open solver ecosystem.\n- Scalability. From workstation to factory scale, and one day, city scale: thousands of interacting entities, batched across environments, without losing physical soundness.\n\nOur ambition is to establish Genesis-World as the go-to simulator for physical AI, from companies and research labs to individuals.\n\nThe problems waiting for you\n\n- Every fidelity for every physics. The same physics at every point of the speed-accuracy spectrum, from heavily batched training with XPBD or VBD to final validation with IPC. Same scene, same API, pick your tradeoff.\n- Invent physics level-of-detail (LOD). Rendering has had LOD for decades, physics is still waiting. Simulate at full fidelity what agents interact with and see, coarsely what they do not.\n- Heterogeneous environments. Every parallel world can hold a completely different model: different bodies, joints, and collision geometries.\n- Adaptive timesteps per island. Error-based control with Runge-Kutta Dopri5, and Time-of-Impact stepping during collision detection, as done in Jiminy.\n- Couple everything, exactly. Efficient and accurate two-way constraint-based coupling between heterogeneous grey-box solvers, using state-of-the-art methods like ADMM. Owning every solver in the stack is what makes it possible.\n- More scalable constraint solvers. Push rigid constraint solving beyond its current scalability ceiling ( reference ).\n- Unify contact resolution. Hydro-elastic compliance, unilateral constraints, and sequential impulses in the same framework, ideally under one generic formulation.\n- Closed kinematic loops without constraints. Handle loops intrinsically for numerical stability and speed, in the spirit of Kamino .\n\nDay 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.\n\nWho you are\n\nYou 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. No blind spots: you relentlessly hunt down even the defect that looks insignificant, because it never is.\n\n- A strong background in physics-based simulation, preferably related to robotics: RBD, FEM, MPM, SPH, IPC, XPBD, VBD, ABD, plus constrained optimization and numerical integration of stiff systems.\n- A track record of shipping simulation code that others rely on, in an engine, in industry, or in a research codebase used beyond its authors.\n- Solid HPC programming (CPU and/or GPU), and an instinct for what makes a numerical method fast in practice, beyond complexity classes.\n- Rigor in validation: analytical closed forms, cross-engine consistency, real-world data.\n\nBonus points: publications in simulation, graphics, or robotics venues (SIGGRAPH, ICRA, IROS, CoRL, RSS). Contributions to an open-source physics engine.", "url": "https://wpnews.pro/news/genesis-world-core-physics-engineer-genesis-ai", "canonical_source": "https://frontierroles.com/jobs/genesis-genesis-world-core-physics-engineer-af920b/", "published_at": "2026-08-19 00:45:30+00:00", "updated_at": "2026-08-19 02:11:43.737830+00:00", "lang": "en", "topics": ["artificial-intelligence", "robotics", "ai-infrastructure", "ai-research"], "entities": ["Genesis AI", "Genesis-World", "Nyx", "Quadrants"], "alternates": {"html": "https://wpnews.pro/news/genesis-world-core-physics-engineer-genesis-ai", "markdown": "https://wpnews.pro/news/genesis-world-core-physics-engineer-genesis-ai.md", "text": "https://wpnews.pro/news/genesis-world-core-physics-engineer-genesis-ai.txt", "jsonld": "https://wpnews.pro/news/genesis-world-core-physics-engineer-genesis-ai.jsonld"}}