Senior AI Compiler Engineer, Algorithms and Code-Generation

NVIDIA•Austin, CA

28d

About The Position

NVIDIA is transforming computer graphics, PC gaming, and accelerated computing. We are tapping into the unlimited potential of AI to define the next era of computing, where our GPU acts as the brains of computers, robots, and self-driving cars. We are looking for an AI & Deep Learning Compiler Engineer to join our Deep Learning & AI Compiler (DLC) team. GPUs are powering a revolution in deep learning, enabling breakthroughs in areas like large language models, generative AI, recommendation systems, image classification, and speech recognition. Our DLC has been the backbone of NVIDIA’s inference engine, spanning data centers, personal devices, automotive, and robotics. The compiler must deliver leading inference performance, fast build time, reduced memory footprints, and ease of use in both Ahead-of-Time and Just-in-Time forms. Join the team building the DLC that will be used by the entire deep learning community.

Requirements

Bachelor’s, master’s or Ph.D. in Computer Science, Computer Engineering, related field or equivalent experience.
3+ years of relevant work or research experience in performance analysis and compiler optimizations.
Experience with compiler technologies (e.g., MLIR, LLVM, XLA, Triton, etc.).
Excellent C/C++ and Python programming and software design skills, including debugging, performance analysis, and test design.
Ability to work independently, define project goals and scope, and lead your own development efforts.
Strong interpersonal skills are required along with the ability to work in a dynamic product-oriented team.

Nice To Haves

Proficient in CPU and/or GPU architecture especially modern Nvidia GPUs like Hopper and Blackwell.
Understanding of deep learning models, algorithms, and frameworks, such as PyTorch, JAX.
GPU kernel authoring and performance analysis using tools such as Nsight Compute.
A track record of success in mentoring early-career engineers and interns is a bonus.
Track record on new hardware bring-up is a plus.

Responsibilities

Analyzing deep learning networks and developing compiler optimization algorithms.
Strong programming skills in CUDA including analyzing and debugging performance bottlenecks on GPUs.
Defining public APIs, performance optimizations and analysis.
Crafting and implementing compiler techniques for AI workloads and future NVIDIA GPUs.