Robotics Physics Simulation Scientist for Synthetic Data

Collinear AI; United States; On-site; USD 150,000 - 230,000; Full time

Job summary

Collinear AI seeks an Applied Scientist to design and optimize a next-generation simulation stack for synthetic data generation. You will drive physics solver innovation, implement high-fidelity models, and collaborate with GPU optimization teams to maximize throughput and fidelity in large-scale environments.

The role demands deep physics expertise, hands-on experience with parallel GPU architectures, and strong proficiency in C++ and Python to deliver production-quality, high-throughput

Qualifications

• MS or PhD in Physics, CS, Applied Math, Engineering, or equivalent hands-on industry experience in physics-based simulation.

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: Design simulation environments, domain randomization strategies, and physics parameter ranges for high-utility training datasets.
• Procedural Scene & Scale Integration: 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 pipelines.
• Technical Leadership: Help shape Collinear's long-term roadmap for high-fidelity physical modeling, differentiable simulation, and synthetic data engine architecture.

Skills

C++

Python

GPU optimization

Physics simulation

ML pipelines

Parallel computing

Differentiable simulation

Education

MS or PhD in Physics, CS, Applied Math, or Engineering

Tools

CUDA

Job description

Collinear AI seeks an Applied Scientist to design and optimize a next-generation simulation stack for synthetic data generation. You will drive physics solver innovation, implement high-fidelity models, and collaborate with GPU optimization teams to maximize throughput and fidelity in large-scale environments.

The role demands deep physics expertise, hands-on experience with parallel GPU architectures, and strong proficiency in C++ and Python to deliver production-quality, high-throughput