Senior Software Engineer — cuEquivariance

Details

NVIDIA BioNeMo is building the computational foundation for the next generation of biological discovery. The cuEquivariance team develops 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 at scale. The team builds production GPU kernels and software interfaces for equivariant deep learning, spanning CUDA kernel engineering, Python library development with PyTorch and JAX, and collaboration with research teams and external framework developers.

Responsibilities

• Build, implement, and optimize CUDA kernels for equivariant neural network primitives, including tensor products, segmented polynomials, and triangle-based operations, targeting peak performance across NVIDIA GPU generations.
• Deliver GPU-accelerated geometric machine learning primitives end to end, from implementation through validated, production-quality software used by external frameworks.
• Build and maintain PyTorch and JAX interfaces 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 interfaces and ensure software integrates cleanly into production pipelines.

Requirements

• 6+ years of software engineering experience with a strong background in CUDA and GPU programming.
• Deep proficiency in C++ and Python, with experience building and shipping production libraries used by external developers.
• A strong foundation in GPU computing, including memory hierarchy, warp-level execution, occupancy, and performance profiling methodology.
• Experience building or contributing to production scientific software libraries, machine learning frameworks, or developer-facing GPU APIs.
• Familiarity with geometric machine learning concepts, including equivariance, group representations, irreducible representations, or tensor products.
• BS or MS in Computer Science, Physics, Applied Mathematics, or a related field, or equivalent experience.

Preferred Qualifications

• Experience contributing to or deeply using equivariant neural network frameworks such as 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 machine learning workloads.
• PhD or equivalent experience in computational chemistry, biophysics, physics, or computer science focused on geometric deep learning or high-performance computing.
• Contributions to open-source geometric machine learning or GPU computing projects.

Compensation And Benefits

The base salary range is USD 184,000–287,500 for Level 4 and USD 224,000–356,500 for Level 5. Base salary is determined by location, experience, and compensation for similar positions. The role also includes eligibility for equity and benefits.

Applications will be accepted at least until May 26, 2026. This posting is for an existing vacancy. NVIDIA uses AI tools in its recruiting processes and is an equal opportunity employer.