Senior Software Engineer — cuEquivariance

NVIDIA BioNeMo is building the computational foundation for the next generation of biological discovery. We are looking for a Senior Software Engineer to join the cuEquivariance team – an NVIDIA library that accelerates geometric neural networks on NVIDIA GPUs, enabling researchers in molecular biology, materials science, physics to train and deploy equivariant models at scale. The team builds and ships GPU kernels and software interfaces that power equivariant deep learning throughout scientific fields. This role spans CUDA kernel engineering, Python library development (PyTorch & JAX), and collaboration with research teams and external framework developers. It is a role for those who want to work where GPU computing meets graph‑based deep learning and impact production pipelines across science. 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 generations. 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. Build and maintain the interfaces for PyTorch and JAX that expose cuEquivariance 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. NVIDIA uses AI tools in its recruiting processes. Compensation and Benefits Your base salary will be determined based on your location, experience, and comparable positions. The base salary range is 184,000 USD‑287,500 USD for Level 4, and 224,000 USD‑356,500 USD for Level 5. You will also be eligible for equity and benefits. Learn more about our benefits at Applications for this job will be accepted at least until May 26, 2026. Equal Opportunity Employer NVIDIA is committed to fostering a diverse work environment and proudly serves as an equal opportunity employer. NVIDIA does not discriminate on the basis of race, religion, color, national origin, gender, gender expression, sexual orientation, age, marital status, veteran status, disability, or any other characteristic protected by law. #J-18808-Ljbffr Nvidia Corporation