Senior Software Engineer — Cuequivariance

NVIDIA NVIDIA · Semiconductors · Santa Clara, CA

Senior Software Engineer to join the cuEquivariance team, which builds and ships production GPU kernels and software interfaces for equivariant deep learning. The role involves CUDA kernel engineering, Python library development (PyTorch/JAX), and collaboration with research teams and external framework developers to accelerate geometric neural networks on NVIDIA GPUs.

What you'd actually do

  1. Build, implement, and optimize CUDA kernels for equivariant neural network primitives — tensor products, segmented polynomials, and triangle-based operations — targeting peak performance across NVIDIA GPU generations.
  2. Be responsible for the end-to-end delivery of GPU-accelerated geometric ML primitives: from implementation to validated, production-quality software that external frameworks depend on.
  3. Build and maintain the interfaces for PyTorch and JAX that expose cuEquivariance primitives to application developers and researchers.
  4. Drive CI/CD infrastructure for multi-GPU kernel builds, automated correctness testing, and performance regression tracking.
  5. Collaborate with Applied Science and research teams to evaluate new equivariant architectures and translate prototypes into production kernels.

Skills

Required

  • 6+ years of software engineering experience
  • CUDA and GPU programming
  • C++
  • Python
  • GPU computing: memory hierarchy, warp-level execution, occupancy, and performance profiling methodology
  • building and shipping production libraries used by external developers
  • building or chipping in to production scientific software libraries, ML frameworks, or developer-facing GPU APIs
  • geometric machine learning — equivariance, group representations, irreducible representations, or tensor products

Nice to have

  • chipped in to or deeply used a major neural network framework that respects equivariance: e3nn, MACE, NequIP, SE(3)-Transformers, or similar
  • Triton kernel development or other GPU kernel authoring tools alongside CUDA
  • mixed-precision or tensor-core-aware algorithm design for scientific or ML workloads
  • PhD or equivalent experience in computational chemistry, biophysics, physics, or computer science with a focus on geometric deep learning or HPC
  • Contributions to open-source geometric ML or GPU computing projects

What the JD emphasized

  • production libraries used by external developers
  • production scientific software libraries
  • production pipelines

Other signals

  • production GPU kernels
  • equivariant deep learning
  • geometric ML primitives
  • external frameworks