MTS - Research (Robotics Simulation Data)

Key Responsibilities: Physics Solver Innovation: Improve and develop advanced physics solvers and numerical modeling methods for high-degree-of-freedom interactions, complex contact dynamics, deformables, and fluids. Training Data Optimization: Partner with team members to design simulation environments, domain randomization strategies, and physics parameter ranges that yield high-utility training datasets for downstream AI models. Procedural Scene & Scale Integration: Collaborate with scene-generation engineers to scale environment variations, physical property distributions, and dynamic multi-agent interactions across billions of simulation steps. Performance Profiling: Profile and optimize simulation execution at scale, minimizing latency and memory overhead in multi-GPU, parallelized rollout pipelines. Technical Leadership: Help shape Collinear’s long-term roadmap for high-fidelity physical modeling, differentiable simulation, and synthetic data engine architecture.

Education: MS or PhD in Physics, Computer Science, Applied Math, Engineering, or equivalent hands-on industry experience in physics-based simulation. Physics Engine Expertise: Strong track record of developing or extending high-fidelity physics engines, rigid-body dynamics solvers, or constraint-based systems. Experience with deformables, fluids, soft bodies, or differentiable simulation is a plus. Numerical Methods & Dynamics: Deep understanding of classical mechanics, contact modeling, constraint solvers, integrators, and managing accuracy-vs-speed trade-offs for large-scale computation. GPU Programming: Advanced expertise in CUDA and GPU optimization, with a proven ability to accelerate numerical algorithms on parallel hardware architectures. Systems Engineering: High proficiency in C++ and Python, with a track record of building reliable, high-throughput software used in production data or ML pipelines. ML Infrastructure Familiarity: Strong grasp of how ML frameworks consume simulation outputs (e.g., vectorized environments, massively parallel rollouts, synthetic dataset generation).Fidelity Intuition: Deep intuition for physical realism and dataset distribution drift - understanding how synthetic physical data behaves and how to model environmental variance without relying on physical hardware collection. Track Record: Publications, open-source contributions, or shipped commercial systems in numerical computing, graphics, physics simulation, or synthetic data generation.