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# Senior Software Engineer - cuEquivariance

- **Company:** NVIDIA Ltd.
- **Location:** Santa Clara, CA, United States
- **Experience:** Expert
- **Salary:** $184,000.0 - $287,500.0
- **Contract:** Permanent contract
- **Skills:** Application Programming Interfaces (APIs), Artificial Intelligence, Algorithm Design, Artificial Neural Networks, C++ (Programming Language), Nvidia CUDA, Continuous Integration, General-Purpose Computing on Graphics Processing Units, Python (Programming Language), Linux Kernel, Machine Learning, Open Source Technology, Tensorflow, Software Engineering, Scripting, Graphics Processing Unit (GPU), Pytorch, Deep Learning, Gpu Programming, Information Technology, Machine Learning Operations, Software Library
- **Published:** October 30, 2026
- **Apply:** https://www.careerbuilder.com/job-details/senior-software-engineer-cuequivariance-santa-clara-ca--365956cf-b828-4f75-a87a-d6d2296f64ef

## About the Role

Software Engineering, Autonomous Driving Systems, GPU (Graphics Processing Unit), Artificial Intelligence (AI), Neural Networks, Molecular Biology, Deep Learning, Software Engineering, Python Programming/Scripting Language, CUDA (Compute Unified Device Architecture), * 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.

## Description

* 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.