NVIDIA

Artificial Intelligence

SeniorDeepLearningCompilerEngineer

$152–242k Santa Clara, California, United States FULL TIME Remote Friendly
Market Sentiment
HIGH DEMAND

Neural analysis suggests this role is
optimal for Senior candidates.

The Brief

“Senior Deep Learning Compiler Engineer at NVIDIA. Skills: Deep Learning Compiler (DLC), compiler optimization algorithms, performance analysis, C/C++, Python. Analyzing deep learning networks. developing compiler optimization algorithms”

What You'll Achieve.

deliver leading inference performance; fast build time; reduced memory footprints; ease of use in the forms of both Ahead-of-Tine and Just-in-Time

Industry & Context.

Artificial Intelligence

What They're Looking For.

Must Have

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, interpersonal skills are required along with the ability to work in a dynamic product-oriented team

Nice to Have

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 the following technologies: 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

What You'll Do.

Analyzing deep learning networks

developing compiler optimization algorithms

performance optimizations and analysis

crafting and implementing compiler infrastructure techniques for neural networks

general software engineering work

How You'll Work.

Team & Collaboration

Collaborating with members of the deep learning software framework teams; Collaborating with the hardware architecture teams

Process & Methodology

define project goals and scope, lead your own development efforts

Full Job Description

NVIDIA has been transforming computer graphics, PC gaming, and accelerated computing for more than 25 years. It’s a unique legacy of innovation that’s fueled by great technology—and amazing people. Today, we’re tapping into the unlimited potential of AI to define the next era of computing. An era in which our GPU acts as the brains of computers, robots, and self-driving cars that can understand the world. Doing what’s never been done before takes vision, innovation, and the world’s best talent. As an NVIDIAN, you’ll be immersed in a diverse, supportive environment where everyone is inspired to do their best work. Come join the team and see how you can make a lasting impact on the world We are looking for a Deep Learning Compiler Engineer. NVIDIA is hiring software engineers for its Deep Learning Compiler (DLC) team. Academic and commercial groups around the world are using GPUs to power a revolution in deep learning, enabling breakthroughs in many areas, e.g. large language models, generative AIs, recommendation systems, image classification, speech recognition, etc. Our DLC has been the backbone of NVIDIA inference engine, spanning across 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 the forms of both Ahead-of-Tine and Just-in-Time. Join the team building the DLC which will be used by the entire deep learning community. **What you 'll be doing:** * 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. * Scope of these efforts includes defining public APIs, performance optimizations and analysis, crafting and implementing compiler infrastructure techniques for neural networks, and other general software engineering work. **What we need to s

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