Senior Compiler Engineer - DL

NVIDIA•Austin, CA

About The Position

NVIDIA is looking for a Deep Learning Compiler Engineer to join its Deep Learning Compiler (DLC) team. Academic and commercial groups worldwide are using GPUs to power a revolution in deep learning, enabling breakthroughs in areas like large language models, generative AIs, recommendation systems, image classification, and speech recognition. The DLC is the backbone of NVIDIA's inference engine, used across data centers, personal devices, automotive, and robotics. The compiler must deliver leading inference performance, fast build times, reduced memory footprints, and ease of use through both Ahead-of-Time and Just-in-Time compilation. Join the team building the DLC that will be used by the entire deep learning community.

Requirements

Bachelors, Masters 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.
Ability to work independently, define project goals and scope, and lead your own development efforts.
Excellent C/C++ and Python programming and software design skills, including debugging, performance analysis, and test design.
Strong interpersonal skills along with the ability to work in a dynamic product-oriented team.

Nice To Haves

Proficient in GPU and NPU architecture.
Experiences in systems with constrained resources, such as embedded platforms, small memory size, and cross compilation.
Experience with the following technologies: MLIR, XLA, TVM, LLVM, deep learning models and algorithms, and deep learning frameworks, such as PyTorch.
Experience mapping AI models to execute on custom NPU solutions.
A track record of success in mentoring junior engineers and interns.

Responsibilities

Analyzing deep learning networks and developing compiler optimization algorithms.
Collaborating with members of the deep learning software framework teams and the hardware architecture teams to accelerate the next generation of deep learning software.
Defining public APIs, performance optimizations and analysis.
Crafting and implementing compiler infrastructure techniques for neural networks.
General software engineering work.