Senior Software Engineer — cuEquivariance

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NVIDIA Bio Ne Mo  is building the computational foundation for the next generation of biological discovery. We are looking for a Senior Software Engineer to join the cu Equivariance team — an NVIDIA library that accelerates geometric neural networks on NVIDIA GPUs, enabling researchers in molecular biology, materials science, and physics to train and deploy equivariant models s team builds and ships the production GPU kernels and software interfaces that power equivariant deep learning throughout the scientific field.

The work spans CUDA kernel engineering, Python library development involving both PyTorch and JAX, and direct collaboration with research teams and external framework developers. If you want to work where GPU computing meets graph-based deep learning, this is the role for you. Your work will run in production pipelines across the scientific community.

What You Will Be Doing:

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  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.

Build and maintain the interfaces for PyTorch and JAX that expose cu Equivariance primitives to application developers and researchers.

Drive CI/CD infrastructure for multi-GPU kernel builds, automated correctness testing, and performance regression tracking.

Collaborate with Applied Science and research teams to evaluate new equivariant architectures and translate prototypes into production kernels.

Engage directly with third-party framework developers and partners to align on interfaces and ensure delivered software integrates cleanly into production pipelines.

What We Need to See:6+ years of software engineering experience with a strong background in CUDA and GPU programming.

Deep proficiency in C++ and Python; experience building and shipping production libraries used by external developers.

Good foundation in GPU computing: memory hierarchy, warp-level execution, occupancy, and performance profiling methodology.

Experience building or chipping in to production scientific software libraries, ML frameworks, or developer-facing GPU APIs.Familiarity with concepts in geometric machine learning — equivariance, group representations, irreducible representations, or tensor products — sufficient to work efficiently in the domain.

BS/MS in Computer Science, Physics, Applied Mathematics, or a related field, or equivalent experience.

Ways to Stand Out from the Crowd:

You have chipped in to or deeply used a major neural network framework that respects equivariance: e3nn, MACE, NequIP, SE(3)-Transformers, or similar.

Hands-on experience with Triton kernel development or other GPU kernel authoring tools alongside CUDA.

Experience with 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.

Widely considered to be one of the technology world’s most desirable employers, NVIDIA offers highly competitive salaries and a comprehensive benefits package. As you plan your future, see what we can offer to you and your family  base…