Senior GPU Simulation Performance Engineer — Equity

NVIDIA Corporation; Santa Clara (CA); On-site; USD 220,000 - 430,000; Full time

Job summary

NVIDIA Corporation seeks an experienced engineer to optimize physics and robotics simulation workloads on NVIDIA GPUs. You will work at the intersection of algorithms, mathematics, GPU architecture, and production software to turn insights into durable code for NVIDIA engineering teams.

Expect to lead performance strategies, develop benchmarks, profile diverse applications, and collaborate across teams to remove bottlenecks.

Qualifications

• 12+ years of relevant experience in high-performance computing, robotics, or simulation.
• Degree in Computer Science, Applied Mathematics, Physics, Mechanical Engineering, Computational Science, or related field.
• Proven stewardship of a consequential open-source engine, library, standard, benchmark, or framework.
• Track record of delivering large, reproducible performance improvements in production numerical, simulation, graphics, CAE, robotics, or scientific-computing systems.
• Expert modern C++ and GPU parallel-programming ability with strong math/physics foundations.

Responsibilities

• Develop performance strategies for physics and robotics workloads on NVIDIA GPUs.
• Build benchmarks and performance models; profile applications from system to GPU kernels.
• Improve algorithms, data structures, memory layouts, and parallel execution with partner developers.
• Collaborate with engineering teams to address bottlenecks rather than duplicating roles.
• Apply reinforcement learning and robotics simulation knowledge to project challenges.
• Write production C++, CUDA, Python, and domain-specific GPU code for simulation platforms.

Skills

C++

GPU parallel programming

CUDA

Python

GPU profiling

Education

Computer Science

Tools

MuJoCo

JAX

PhysX

CUDA

Job description

NVIDIA Corporation seeks an experienced engineer to optimize physics and robotics simulation workloads on NVIDIA GPUs. You will work at the intersection of algorithms, mathematics, GPU architecture, and production software to turn insights into durable code for NVIDIA engineering teams.

Expect to lead performance strategies, develop benchmarks, profile diverse applications, and collaborate across teams to remove bottlenecks.