NVIDIA's invention of the GPU 1999 sparked the growth of the PC gaming market, redefined modern computer graphics, and revolutionized parallel computing. More recently, GPU deep learning ignited modern AI — the next era of computing — with the GPU acting as the brain of computers, robots, and self-driving cars that can perceive and understand the world. Today, we are increasingly known as “the AI computing company”.
We are looking for an AI & Deep Learning Compiler Engineer. NVIDIA is hiring software engineers for its Deep Learning & AI 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. With the rapid advancement of AI, our DLC has been the backbone of NVIDIA’s 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-Time 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:
Develop compiler IR, programming model and optimizations for future GPU architectures.
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 optimizations and kernel generation for neural networks, and other general software engineering work.
What we need to see:
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.
Experience with compiler technologies (e.g., MLIR, XLA, and LLVM 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 fast moving & dynamic product-oriented team.
Ways to stand out from the crowd:
Understanding of deep learning models, algorithms and frameworks, such as PyTorch, XLA etc.
Understanding of LLM inference optimizations and techniques.
GPU kernel generation with high performance and fast build time.
Proficient in GPU architecture. CUDA or OpenCL programming experience.
Track record on new hardware bring-up is a plus.
With competitive salaries and a generous benefits package, we are widely considered to be one of the technology world’s most desirable employers. We have some of the most forward-thinking and hardworking people in the world working for us and, due to unprecedented growth, our exclusive engineering teams are rapidly growing. If you're a creative and autonomous engineer with a real passion for technology, we want to hear from you.
Your base salary will be determined based on your location, experience, and the pay of employees in similar positions. The base salary range is 152,000 USD - 241,500 USD.You will also be eligible for equity and benefits.
This posting is for an existing vacancy.
NVIDIA uses AI tools in its recruiting processes.
NVIDIA is committed to fostering a diverse work environment and proud to be an equal opportunity employer. As we highly value diversity in our current and future employees, we do not discriminate (including in our hiring and promotion practices) on the basis of race, religion, color, national origin, gender, gender expression, sexual orientation, age, marital status, veteran status, disability status or any other characteristic protected by law.Skills Required
- 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, XLA, LLVM).
- 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 development efforts.
- Strong interpersonal skills and ability to work in a fast-moving, product-oriented team.
- Understanding of deep learning models, algorithms and frameworks (e.g., PyTorch, XLA).
- Understanding of LLM inference optimizations and techniques.
- GPU kernel generation with high performance and fast build time.
- Proficiency in GPU architecture; CUDA or OpenCL programming experience.
- Track record on new hardware bring-up.
NVIDIA Compensation & Benefits Highlights
The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about NVIDIA and has not been reviewed or approved by NVIDIA.
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Equity Value & Accessibility — Equity awards and a discounted ESPP are highlighted as core parts of total compensation, enabling employees to share in the company’s success. Stock-based compensation and the two-year lookback ESPP are consistently described as especially valuable.
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Healthcare Strength — Health coverage is portrayed as robust, with comprehensive medical, dental, and vision options alongside mental health support and on-site care resources. Employer HSA contributions and wellness perks reinforce the depth of the offering.
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Retirement Support — Retirement programs are depicted as strong, featuring a meaningful 401(k) match with Roth options and support for Mega Backdoor Roth contributions. These elements position long-term savings as a notable advantage of the total rewards package.
NVIDIA Insights
What We Do
NVIDIA’s invention of the GPU in 1999 sparked the growth of the PC gaming market, redefined modern computer graphics, and revolutionized parallel computing. More recently, GPU deep learning ignited modern AI — the next era of computing — with the GPU acting as the brain of computers, robots, and self-driving cars that can perceive and understand the world. Today, NVIDIA is increasingly known as “the AI computing company.”
