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 @ 4
Debugging @ 4
API @ 4
PyTorch @ 4
CUDA @ 7
GPU @ 6
Deep Learning @ 4
AI @ 4
Robotics @ 4
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 has been transforming computer graphics, PC gaming, and accelerated computing for more than 25 years. Today, NVIDIA is tapping into the unlimited potential of AI to define the next era of computing. The Deep Learning & AI Compiler (DLC) team develops compilers used across data centers, personal devices, automotive, and robotics to deliver leading inference performance, fast build time, reduced memory footprints, and ease of use for both Ahead-of-Time and Just-in-Time workflows.
Responsibilities
- Analyze deep learning networks and develop compiler optimization algorithms.
- Define public APIs, implement performance optimizations and analysis, and craft compiler techniques for AI workloads and future NVIDIA GPUs.
- Analyze and debug performance bottlenecks on GPUs; implement solutions to improve inference performance, build time, and memory usage.
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 (examples given: MLIR, LLVM, XLA, Triton).
- Excellent C/C++ and Python programming and software design skills, including debugging, performance analysis, and test design.
- Strong programming skills in CUDA, including analyzing and debugging performance bottlenecks on GPUs.
- Ability to work independently, define project goals and scope, and lead development efforts.
- Strong interpersonal skills and ability to work in a dynamic product-oriented team.
Ways to stand out
- Proficiency in CPU and/or GPU architecture, especially modern NVIDIA GPUs like Hopper and Blackwell.
- Understanding of deep learning models, algorithms, and frameworks such as PyTorch and JAX.
- GPU kernel authoring and performance analysis using tools such as Nsight Compute.
- Track record of mentoring early-career engineers and interns.
- Experience with new hardware bring-up.
Compensation and benefits
- Base salary range: 152,000 USD - 241,500 USD (determined based on location, experience, and pay of employees in similar positions).
- Eligibility for equity and benefits (link to NVIDIA benefits referenced in original posting).
Additional information
- Applications for this job will be accepted at least until July 18, 2026.
- This posting is for an existing vacancy.
- NVIDIA uses AI tools in its recruiting processes.
- NVIDIA is an equal opportunity employer and committed to an inclusive work environment.
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