Senior Deep Learning Compiler Engineer

NVIDIA•Austin, WA

7d•Onsite

About The Position

NVIDIA is looking for a Deep Learning Compiler Engineer to join its Deep Learning Compiler (DLC) team. This role involves contributing to a compiler that is crucial for NVIDIA's inference engine, used across data centers, personal devices, automotive, and robotics. The compiler aims to deliver leading inference performance, fast build times, reduced memory footprints, and ease of use for both Ahead-of-Time and Just-in-Time compilation. The DLC is used by the entire deep learning community and enables breakthroughs in areas like large language models, generative AIs, recommendation systems, image classification, and speech recognition.

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 and the ability to work in a dynamic product-oriented team.

Nice To Haves

Proficient in CPU and/or GPU architecture.
CUDA or OpenCL programming experience.
Experiences in systems with constrained resources, such as embedded platforms, small memory size, and cross compilation.
Experience with MLIR, XLA, TVM, LLVM, deep learning models and algorithms, and deep learning frameworks, such as PyTorch.
GPU kernel generation with high performance and fast build time.
A track record of success in mentoring junior engineers and interns is a bonus.

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.
Performing other general software engineering work.