3 days ago
Base Salary
$152k - $288k/yr
Responsibilities
- Build and support Transformer Engine, an open-source library for accelerating large language model training.
- Conduct systems research and implement performance improvements involving low precision, parallelism, and related deep learning techniques.
- Implement, benchmark, and optimize deep learning models such as LLMs for NVIDIA GPUs and systems.
- Contribute to NVIDIA submissions for community benchmarks including MLPerf.
- Collaborate with the open-source community and support enterprise customers and partners.
- Influence the design of future NVIDIA hardware generations and core platform software components.
Requirements
- Bachelor’s degree or equivalent experience in Computer Science, Electrical Engineering, or a related field.
- At least 3 years of experience programming in C++ and Python.
- Strong background, experience, or coursework in parallel systems programming, preferably on GPUs.
- Knowledge of computer architecture, code optimization, and/or operating systems.
- Proven experience developing large software projects.
- Excellent verbal and written communication skills.
- Preferred qualifications include experience with PyTorch, JAX, deep learning frameworks, performance analysis, profiling, multi-GPU or multi-node systems, LLM architectures, attention mechanisms, cuBLAS, cuDNN, cuSOLVER, CUDA, OpenAI Triton, CuTeDSL, Pallas, and open-source contributions.
Benefits
- Eligible for equity and benefits.
- Applications will be accepted at least until November 9, 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.