> Markdown version of [/jobs/ext/2282392-senior-software-engineer-cuequivariance](https://www.wearedevelopers.com/jobs/ext/2282392-senior-software-engineer-cuequivariance). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Senior Software Engineer - cuEquivariance - **Company:** NVIDIA Corporation - **Location:** Santa Clara, CA, United States - **Experience:** Expert - **Salary:** $184,000.0 - $287,500.0 - **Contract:** Permanent contract - **Skills:** Application Programming Interfaces (APIs), 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, Pytorch, Deep Learning, Gpu Programming, Information Technology, Software Library - **Published:** August 28, 2026 - **Apply:** https://nvidia.wd5.myworkdayjobs.com/NVIDIAExternalCareerSite/job/US-CA-Santa-Clara/Senior-Software-Engineer---cuEquivariance_JR2018334 ## About the Role * 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. ## Related Videos - [Coffee with Developers - Stephen Jones - NVIDIA](https://www.wearedevelopers.com/videos/1303-coffee-with-developers-stephen-jones-nvidia) - [Photonic Computing: Programming a New Class of AI Accelerators (incl. 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