Jobs / United States / Nvidia Corporation
Software Engineer, CUDA Deep Learning Systems
Nvidia Corporation · 🇺🇸 3 Locations
Sponsorship verdict
Sponsorship possible
One solid signal, not two — worth applying, and worth asking about sponsorship early.
- Employer is on a government sponsor recordThe US Department of Labor certified 2,374 H-1B/E-3 labor condition applications for this employer between Oct 2025 and Jun 2026 (latest Jun 2026) — the step every H-1B hire needs first. USCIS also records 394 H-1B approvals in FY2023. Source: LCA disclosure data (US Department of Labor (OFLC)).
- The posting doesn’t mention sponsorshipSilence isn’t a refusal — ask the recruiter before investing much time.
- No salary bar for this routeH-1B has no fixed salary bar: the employer must pay at least the prevailing wage for the role and area. Cap-subject employers enter a lottery weighted by wage level. Source: https://www.federalregister.gov/documents/2025/12/29/2025-23853/weighted-selection-process-for-registrants-and-petitioners-seeking-to-file-cap-subject-h-1b, rules effective 2026-02-27.
- What Nvidia Corporation paid sponsored hires in similar roles701 certified filings for “Engineer Senior Systems Software” (Software Developers) in CA: $173k–$214k, median $190k. Most were filed at wage level IV (82%) — 4 lottery entries, ≈61% projected selection odds for cap-subject employers. Source: US Department of Labor LCA disclosure data (Oct 2025 – Jun 2026).
- Confirmed live todayWhen a source last listed this job as open.
US H-1B: cap-subject employers enter a lottery weighted by wage level — Level I gets 1 entry, Level IV gets 4 (DHS projected selection odds ≈15% at Level I to ≈61% at Level IV). Universities and non-profit research employers are cap-exempt. The $100,000 fee for new petitions from abroad is currently blocked by a court order (appeal pending).
A verdict summarises public evidence; it is not legal advice and never a guarantee — the employer and the immigration authority decide. Sign in to factor in where you can already work.
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Sponsor Radar — Nvidia Corporation
The US Department of Labor certified 2,374 H-1B/E-3 labor condition applications for this employer between Oct 2025 and Jun 2026 (latest Jun 2026) — the step every H-1B hire needs first. USCIS also records 394 H-1B approvals in FY2023. Source: LCA disclosure data (US Department of Labor (OFLC)).
Past sponsorship or register membership never guarantees sponsorship for this vacancy or for you. Full Sponsor Radar for Nvidia Corporation →
About the role
We are looking for an experienced and highly motivated software professional to work on pioneering initiatives and projects at the intersection of CUDA and Deep Learning Systems. As the complexity and scale of artificial intelligence continue to grow, the intersection of advanced deep learning architectures, massive-scale distributed computing, and low-level hardware optimization has never been more critical. Our team is dedicated to exploring and prototyping next-generation ideas that bridge the gap between deep learning algorithms and CUDA, pushing the boundaries of what is possible on modern accelerator architectures. Join our dynamic, research-oriented team to help unlock maximum hardware performance for emerging AI workloads. You will be a crucial member of a highly technical group exploring uncharted territories in model optimization, custom kernel development, and cluster-scale AI systems design. If you are passionate about the fundamentals of deep learning and thrive on squeezing every ounce of performance out of advanced computing systems from a single GPU to supercomputer clusters, we want you on our team! What you will be doing: • Explore, research, and prototype novel systems optimizations for advanced deep learning models at the intersection of high-level DL frameworks and low-level CUDA through modeling, simulation, and silicon prototyping. • Architect and optimize distributed computing systems that scale seamlessly from a single node to massive, cluster-scale supercomputing environments. • Design, implement, and optimize custom high-performance CUDA kernels tailored to emerging neural network architectures and workloads. • Analyze complex hardware-software interactions to identify and resolve performance bottlenecks in both training and inference pipelines. • Collaborate closely with AI researchers, HW and SW architects, kernel and compiler authors and CUDA driver experts to co-design systems and algorithms that improve accelerator compute utilization, memory bandwidth, cross-node network communication efficiency and programmability. • Develop exploratory tools and runtime systems to profile and accelerate new paradigms in deep learning. • Write clean, effective, and maintainable code, ensuring exploratory prototypes can smoothly transition into open-source releases, upstream framework integrations, internal tools, or closed-source commercial products. What we need to see: • BS, MS, or PhD degree in Computer Science, Computer Engineering, Electrical Engineering, or related field (or equivalent experience). • 2+ years of relevant industry experience or equivalent academic experience after degree achievement. • Strong proficiency in C++ and Python programming. • Solid background in the fundamentals of Deep Learning with a focus on transformers. • Strong understanding of distributed computing principles, multi-node scaling, and the unique performance challenges of cluster-scale execution. • Proven experience in systems programming, computer architecture, and low-level systems performance optimization. • Familiarity with deep learning accelerator architectures such as the GPU and hands-on experience with CUDA programming, kernel optimization, and workload profiling • Experience profiling and optimizing generative AI models, including but not limited to, pioneering large language models. • Research background in machine learning systems or adjacent fields and experience profiling and optimizing innovative vision models, generative AI architectures, or diffusion models. • A track-record of initiative and willingness to deep-dive on problems across the stack. Ways to stand out from the crowd: • Deep expertise in performance internals and execution graphs of major deep learning training and inference fr