2 days ago
Base Salary
$152k - $242k/yr
Responsibilities
- Analyze deep learning networks and develop compiler optimization algorithms.
- Collaborate with deep learning software framework and GPU architecture teams to accelerate next-generation deep learning software.
- Define public APIs and implement compiler techniques for AI workloads and future NVIDIA GPUs.
- Perform performance analysis and optimize inference performance, build time, memory footprint, and usability.
- Independently define project goals and scope and lead development efforts.
Requirements
- Bachelor’s, Master’s, or Ph.D. in Computer Science, Computer Engineering, a related field, or equivalent experience.
- At least 3 years of relevant work or research experience in performance analysis and compiler optimizations.
- Experience with compiler technologies such as MLIR, LLVM, XLA, or Triton.
- Strong C/C++ and Python programming, software design, debugging, performance analysis, and test design skills.
- Ability to work independently and define project goals and scope.
- Strong interpersonal skills and ability to work in a dynamic product-oriented team.
- Preferred qualifications include CPU or GPU architecture knowledge; CUDA or OpenCL programming; understanding of deep learning models, algorithms, and frameworks such as PyTorch and JAX; GPU kernel authoring and performance analysis with Nsight Compute; hardware bring-up; and mentoring early-career engineers and interns.
Benefits
- Competitive salary and a generous benefits package.
- Eligible for equity and benefits.
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.