Genesis-World: Core Physics Engineer

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.

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.

Heterogeneous environments. Every parallel world can hold a completely different model: different bodies, joints, and collision geometries.

Adaptive timesteps per island. Error-based control with Runge-Kutta Dopri5, and Time-of-Impact stepping during collision detection, as done in Jiminy.

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.

More scalable constraint solvers. Push rigid constraint solving beyond its current scalability ceiling ( reference ).

Unify contact resolution. Hydro-elastic compliance, unilateral constraints, and sequential impulses in the same framework, ideally under one generic formulation.

Closed kinematic loops without constraints. Handle loops intrinsically for numerical stability and speed, in the spirit of Kamino.

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.

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. No blind spots: you relentlessly hunt down even the defect that looks insignificant, because it never is.

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.

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.

Solid HPC programming (CPU and/or GPU), and an instinct for what makes a numerical method fast in practice, beyond complexity classes.

Rigor in validation: analytical closed forms, cross-engine consistency, real-world data.

Bonus points: publications in simulation, graphics, or robotics venues (SIGGRAPH, ICRA, IROS, CoRL, RSS). Contributions to an open-source physics engine.