Used Tools & Technologies
Not specified
Required Skills & Competences
Tag name is followed by "@" symbol and proficiency level value.
About proficiency levels:
- 1-2 — basic awareness. Minimal hands-on experience, and a rudimentary understanding of the technology's purpose;
- 3-6 — daily use. Comfortable and regular usage, capable of handling common tasks and challenges related to the technology;
- 7-9 — you are an expert, you can teach others, you know all the pitfalls and tricks;
- 10 — exceptional knowledge, comprehensive understanding, and adeptness in all aspects of the technology, including advanced problem-solving. Think twice before claiming or demanding such level.
Python @ 4
Algorithms @ 4
Mentoring @ 1
Debugging @ 4
API @ 4
PyTorch @ 4
CUDA @ 6
GPU @ 4
Deep Learning @ 7
AI @ 7
OpenCL @ 6
Performance Analysis @ 4
LLVM @ 4
JAX @ 4
- 1-2 — basic awareness. Minimal hands-on experience, and a rudimentary understanding of the technology's purpose;
- 3-6 — daily use. Comfortable and regular usage, capable of handling common tasks and challenges related to the technology;
- 7-9 — you are an expert, you can teach others, you know all the pitfalls and tricks;
- 10 — exceptional knowledge, comprehensive understanding, and adeptness in all aspects of the technology, including advanced problem-solving. Think twice before claiming or demanding such level.
Details
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, we are increasingly known as “the AI computing company”.
On this team you'll build an MLIR-based AI compiler that powers NVIDIA's inference engine end to end, with a focus on performance, fast builds, low memory use, and Ahead-of-Time and Just-in-Time usability across data center and edge.
Responsibilities
- Develop MLIR-based graph representations and optimizations for future GPU architectures.
- Partner with framework and hardware teams to enable new model patterns and upcoming GPU architectural features.
- Define APIs and MLIR dialects, conduct performance optimizations and analysis, implement compiler optimizations and kernel generation for neural networks.
- Contribute to general software engineering work related to the compiler and runtime.
Requirements
- Bachelor's, Master's, or Ph.D. in Computer Science, Computer Engineering, a related field, or equivalent experience.
- 3+ years of relevant work or research experience in performance analysis and compiler optimizations.
- Experience with compiler technologies such as MLIR, XLA, and LLVM.
- Excellent C/C++ and Python programming and software design skills, including debugging, performance analysis, and testing.
- Ability to work independently, define project goals and scope, and lead your own development efforts.
- Strong interpersonal skills and the ability to thrive 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 and JAX.
- Experience with GPU kernel generation targeting high performance and fast build times.
- Proficiency in GPU architecture with CUDA or OpenCL programming experience.
- A track record of mentoring early career engineers and interns is a bonus.
Compensation
- Base salary range: 152,000 USD - 241,500 USD.
- You will also be eligible for equity and benefits.
Other information
- Applications for this job will be accepted at least until April 26, 2026.
- This posting is for an existing vacancy.
- NVIDIA uses AI tools in its recruiting processes.
- NVIDIA is an equal opportunity employer and is committed to fostering a diverse work environment.