Jobs / United States / Nvidia Corporation
AI Computing Software Development Intern - 2027
Nvidia Corporation · 🇺🇸 2 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).
- Last confirmed live 1 day agoWhen 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
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. Come join the team and see how you can make a lasting impact on the world. What You’ll be doing: As an intern, you’ll focus on one of three specialized tracks: • TensorRT LLM – Inference Optimization (Python / PyTorch): Build and enhance high‑performance LLM inference pipelines. Analyze and optimize model execution, scalability, and memory usage. Collaborate across framework and research teams to deliver efficient multi‑GPU model serving. • TensorRT Compiler – Graph Optimization (C++): Work on the TensorRT compiler backend to improve graph transformations and code generation for NVIDIA GPUs. Develop compiler optimization passes, refine operator fusion and memory allocation, and collaborate with CUDA and hardware architecture teams. • CuTe DSL & CUDA Kernels Development / Optimization (CUDA C++ / Python DSL): Design and tune GPU compute kernels and DSL implementations for core deep learning operations such as GEMM, MoE, Attention and Convolution. Profile, analyze and improve CUDA kernel performance to achieve maximum GPU efficiency. What We need to see: • Pursuing an M.S. or Ph.D. in Computer Science, Computer Engineering, Electrical Engineering, Applied Mathematics, or related field. • Excellent problem‑solving ability, curiosity for cutting‑edge AI systems, and passion for GPU computing and deep learning software performance. • TensorRT LLM: Strong Python programming and experience with PyTorch; solid understanding of inference and GPU acceleration. • TensorRT Compiler: Proficient in C++, with experience in compiler or performance optimization. • CuTe DSL & CUDA Kernels: Skilled in C/C++ and CUDA or parallel programming; familiar with LLVM, MLIR and compiler; understanding of computer architecture and performance profiling/analysis/optimization. Join us and play a part in building the AI computing platforms that drive innovation across industries worldwide.