Senior MLIR AI Compiler Engineer for GPU Inference

NVIDIA; Santa Clara (CA); On-site; USD 152,000 - 288,000; Full time

Benefits offered by this job

Equity

Benefits

Job summary

NVIDIA is seeking a Senior AI Compiler Engineer in California to build an MLIR-based AI compiler powering NVIDIA's inference engine end-to-end, with a focus on performance, fast builds, low memory use, and AoT/JIT usability across data center and edge.

You will collaborate with framework and hardware teams to enable new model patterns and GPU architectural features, define APIs, and contribute to compiler optimizations and kernel generation for neural networks.

Qualifications

• Bachelor's, Master's, or Ph.D. in Computer Science, Computer Engineering, a related field, or equivalent experience.
• 3+ years of relevant work or research experience in performance analysis and compiler optimizations.
• Experience with compiler technologies such as MLIR, XLA, and LLVM.
• Excellent C/C++ and Python programming and software design skills, including debugging, performance analysis, and testing.
• Ability to work independently, define project goals and scope, and lead your own development efforts.
• Strong interpersonal skills and the ability to thrive in a fast-moving, dynamic, product-oriented team.

Responsibilities

• Develop MLIR-based graph representations and optimizations for future GPU architectures.
• Partner with framework and hardware teams to enable new model patterns and upcoming GPU architectural features.
• Define APIs and MLIR dialects, conduct performance optimizations and analysis, implement compiler optimizations and kernel generation for neural networks, and contribute to other general software engineering work.

Skills

C/C++ programming

Python programming

Independent work

Leadership

Education

Bachelor's/Master's/PhD in CS/CE or related

Tools

MLIR

XLA

LLVM

Debugging tools

Job description

NVIDIA is seeking a Senior AI Compiler Engineer in California to build an MLIR-based AI compiler powering NVIDIA's inference engine end-to-end, with a focus on performance, fast builds, low memory use, and AoT/JIT usability across data center and edge.

You will collaborate with framework and hardware teams to enable new model patterns and GPU architectural features, define APIs, and contribute to compiler optimizations and kernel generation for neural networks.