7 days ago
Base Salary
$184k - $357k/yr
Responsibilities
- Integrate new CUDA features and runtime abstractions into AI frameworks from proof of concept through performance analysis and production.
- Analyze AI workloads and frameworks to identify lower-level optimization and innovation opportunities.
- Own improvements to the AI compiler-runtime interface for high-performance multi-GPU and multi-node solutions.
- Design fault-tolerant and elastic solutions for large-scale and dynamic AI workloads.
- Influence the CUDA roadmap and collaborate with researchers, hardware and software architects, compiler authors, kernel developers, and CUDA driver experts.
- Develop tools and runtime systems to profile and accelerate new deep learning paradigms.
- Write maintainable code and transition prototypes into open-source releases, framework integrations, internal tools, or commercial products.
Requirements
- Bachelor’s, master’s, or PhD degree in Computer Science, Computer Engineering, Electrical Engineering, or a related field, or equivalent experience.
- 8+ years of relevant industry experience or equivalent academic experience after completing a degree.
- Development experience with deep learning frameworks and inference engines including PyTorch, JAX, TRT-LLM, vLLM, and SGLang.
- Rapid prototyping and development experience with Python, C++, CUDA, or related domain-specific languages.
- Understanding of AI models, parallelism, compiler technologies, computer architecture, hardware-software interactions, operating systems, and HPC/AI communication concepts.
- Experience benchmarking AI clusters and familiarity with performance profiler toolchains such as PyTorch Profiler or NVIDIA Nsight Systems.
- Preferred experience includes deep learning framework internals, CUDA, NCCL, MPI, UCX, distributed machine learning, deep learning compilers, kernel authoring, and compute-communication overlap.
Benefits
- Equity and benefits are provided.
- Base salary is determined by location, experience, and other objective job-related factors.
- The application deadline is at least November 6, 2026.
Categories
About Nvidia
Nvidia designs and sells GPUs and accelerated computing platforms for data centers, AI/ML, graphics, gaming, and automotive, monetizing through hardware, software platforms (CUDA, AI frameworks), and systems like DGX and networking. Customers include cloud providers, enterprises, researchers, and OEMs. Founded in 1993 and headquartered in Santa Clara, it is a public company traded on NASDAQ under NVDA.