1 day ago
Base Salary
$152k - $242k/yr
Responsibilities
- Develop compiler optimization algorithms for deep learning workloads.
- Optimize JAX and OpenXLA training and inference performance on NVIDIA GPUs.
- Design graph partitioning and tensor sharding techniques for distributed training and inference.
- Perform performance tuning and analysis.
- Implement code generation for NVIDIA GPU backends using open-source compilers.
- Design user-facing features in JAX and related libraries.
- Collaborate with deep learning framework and GPU hardware architecture teams on compiler features for next-generation GPUs.
- Define project goals and scope, lead development efforts, and apply software engineering and testing practices.
- Mentor junior engineers and interns when applicable.
Requirements
- Bachelor's, master's, or Ph.D. in computer science, computer engineering, or a related field, or equivalent experience.
- At least 4 years of relevant work or research experience in performance analysis and compiler optimizations.
- Excellent C/C++ programming and software design skills, including debugging, performance analysis, and test design.
- Strong foundation in CPU, GPU, or high-performance hardware accelerator architecture.
- Knowledge of high-performance computing and distributed programming.
- CUDA or OpenCL programming experience is desired but not required.
- Experience with XLA, TVM, MLIR, LLVM, OpenAI Triton, deep learning models and algorithms, or deep learning framework design is a plus.
- Experience with JAX, PyTorch, or TensorFlow is valued.
- Strong interpersonal skills and the ability to work independently in a dynamic, product-oriented team.
- Experience with CUDA or GPUs and open-source compilers is valued.
Benefits
- Competitive salary and a generous benefits package.
- Eligible for equity and benefits.
- Applications accepted at least until September 20, 2026.
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.