Senior AI Compiler Engineer - MLIR & GPU Optimizations

NVIDIA; Washington; On-site; USD 152,000 - 288,000; Full time

Benefits offered by this job

Equity

Benefits package

Job summary

NVIDIA is seeking a Senior AI Compiler Engineer to build an MLIR-based AI compiler powering the inference engine, with a focus on performance, fast builds, low memory usage, and both Ahead-of-Time and Just-in-Time usability for data center and edge deployments.

You will collaborate with framework and hardware teams, define MLIR dialects, and drive kernel generation and optimizations for neural networks while contributing to broader software engineering tasks.

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++

Python

Performance analysis

Debugging

Independent work

Interpersonal skills

Leadership

MLIR

Education

Bachelor's/Master's/Ph.D. in CS/CE or related field

Tools

MLIR

XLA

LLVM

CUDA/OpenCL

Job description

NVIDIA is seeking a Senior AI Compiler Engineer to build an MLIR-based AI compiler powering the inference engine, with a focus on performance, fast builds, low memory usage, and both Ahead-of-Time and Just-in-Time usability for data center and edge deployments.

You will collaborate with framework and hardware teams, define MLIR dialects, and drive kernel generation and optimizations for neural networks while contributing to broader software engineering tasks.