CUDA/C++ Performance Engineer for Differentiable Physics Simulator - Honda Research Institute USA

CUDA/C++ Performance Engineer for Differentiable Physics Simulator

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CUDA/C++ Performance Engineer for Differentiable Physics Simulator

Job Number: P25T05
​Honda Research Institute USA (HRI-US) is seeking a self-motivated engineer to join our Intelligent Robotics Research division. This individual will improve performance of a CUDA/C++ differentiable physics simulator across the GPU backend, including CUDA kernels, host/device data flow, sparse solver structure, and backward workflows used in optimization. The work will require profiling forward and backward simulation workloads, identifying bottlenecks, improving GPU utilization, and reducing CPU/GPU synchronization and transfer overhead.
San Jose, CA

 

Key Responsibilities

 

  • Profile bottlenecks across CUDA kernels, memory transfers, synchronization, sparse assembly, solver steps, and differentiable rollout paths.
  • Determine the highest-impact performance lever for each bottleneck, whether kernel tuning, data residency, batching, stream usage, solver changes, or reduction/assembly redesign.
  • Improve existing CUDA backend architecture, including host/device data flow, CUDA kernels, sparse assembly, and solver structure.
  • Evaluate tradeoffs between targeted optimization, architectural refactoring, and larger rewrites when justified by profiling evidence.
  • Measure and validate improvements in speed, GPU utilization, correctness, and numerical reproducibility.
  • Deliver results in accordance with project timelines.
  • Prepare written and oral technical reports and demonstrations.
  • Collaborate with our teams of scientists and engineers in Honda’s regional and global R&D offices. Communicate profiling results, tradeoffs, and implementation outcomes to audiences with varying CUDA experience. 

 

Minimum Qualifications

 

  • Strong C++ and CUDA C++ experience in production or research codebases.
  • Proven experience profiling and optimizing CUDA kernels with tools such as NVIDIA Nsight Systems, Nsight Compute, or equivalent GPU profiling workflows.
  • Comfortable editing low-level GPU code involving reductions, atomics, sparse matrices, memory coalescing, launch configuration, and synchronization.
  • Experience reducing CPU/GPU transfer overhead using better data residency, batching, pinned memory, async copies, streams, or kernel fusion.
  • Familiarity with numerical simulation, optimization, or differentiable physics workflows.
  • At least 1 year of hands-on experience with the qualifications above.

 

Bonus Qualifications

  • Experience with contact-rich physics simulation.
  • Experience with differentiable simulation or trajectory optimization.
  • Experience optimizing sparse linear solvers on GPU.
  • Experience tuning CUDA MPS workloads or multi-process GPU scheduling.
  • 3+ years of hands-on experience with the qualifications above preferred.

 

Desired Start Date   8/17/2026
Contract Duration   1 year
Position Keywords  ​CUDA, Differentiable Simulators 

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- A cover letter highlighting relevant background (Optional)

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