4 days ago
Base Salary
$108k - $196k/yr
Responsibilities
- Develop AI systems technologies for efficient deep learning inference and training.
- Design, implement, and optimize GPU kernels for AI workloads.
- Build extensible abstractions for serving engines and training frameworks.
- Develop just-in-time domain-specific compilers and runtimes.
- Collaborate with engineers working on deep learning frameworks, libraries, kernels, and GPU architectures.
- Contribute to open source projects including FlashInfer, cuDNN, vLLM, SGLang, PyTorch, JAX, and XLA.
Requirements
- BS, MS, or PhD in Computer Science, Electrical Engineering, or a related field, or equivalent experience.
- Strong experience developing or using deep learning frameworks such as PyTorch, JAX, TensorFlow, or ONNX, or inference engines and runtimes such as vLLM, SGLang, or MLC.
- Strong Python and C/C++ programming skills.
- Preferred experience with domain-specific compiler and library solutions for LLM inference and training, including FlashInfer or Flash Attention.
- Preferred expertise in training frameworks, inference engines, and machine learning compilers such as Apache TVM or MLIR.
- Preferred GPU kernel development and performance optimization experience using CUDA C/C++, CUTLASS DSL, cuTile, Triton, or similar technologies.
- Open source project ownership or contribution experience is preferred.
Benefits
- Base salary range is $108,000-$178,250 for Level 1 and $124,000-$195,500 for Level 2.
- Eligible for equity and benefits.
- Applications accepted at least until October 15, 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.