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NVIDIA has been transforming computer graphics, PC gaming, and accelerated computing for more than 25 years. It’s a unique legacy of innovation that’s fueled by great technology—and amazing people. Today, we’re tapping into the unlimited potential of AI to define the next era of computing. An era in which our GPU acts as the brains of computers, robots, and self-driving cars that can understand the world. Doing what’s never been done before takes vision, innovation, and the world’s best talent. As an NVIDIAN, you’ll be immersed in a diverse, supportive environment where everyone is inspired to do their best work. Join our group and discover how you can develop a lasting impact on the world. 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, and physics to train and deploy equivariant models at scale. This 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 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. 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 www.nvidiabenefits.com/ Your base salary will be determined based on your location, experience, and the pay of employees in similar 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 . Applications for this job will be accepted at least until August 21, 2026. This posting is for an existing vacancy. NVIDIA uses AI tools in its recruiting processes. NVIDIA is committed to fostering an inclusive work environment and proud to be an equal opportunity employer. As we highly value diversity in our current and future employees, we do not discriminate (including in our hiring and promotion practices) on the basis of race, religion, color, national origin, gender, gender expression, sexual orientation, age, marital status, veteran status, disability status or any other characteristic protected by law.
Here's how to pick the right one and stand out in your application.
144.883Jobs
31.687IN
81%EN
That number is real. WorkMundi's database shows 144,883 open engineer roles across the world. India has the most with 31,687 jobs, followed by the United States with 30,084. If you just finished reading one job ad and felt paralyzed by choice, you're not alone—but this scale is actually an advantage. It means you can afford to be selective.
Start by geography and language. The majority of engineer ads—117,837 of them—have the job posting text written in English. Use that as one filter, but remember: the ad text language tells you nothing about whether the role actually requires you to speak English day-to-day. Read the job description carefully. Then check which countries have the volume you're targeting. Singapore, Poland, and Australia round out the top five after India and the US.
Next, learn who's hiring. Accenture has posted 2,801 engineer roles. andurilindustries, speechify, and jobgether are also actively recruiting. If you're applying to one of these names, research their hiring patterns and interview style before you apply. That homework pays off.
When you interview, expect the question every engineer hears: 'Tell me about a time you had to debug a problem that wasn't in your job description.' Have a specific story ready—not a general one. Name the tools, the deadline pressure, and what you learned. Hiring managers listen for whether you see problem-solving as part of the role itself, not a favour.