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 @ 4
GPU @ 6
Deep Learning @ 4
AI @ 4
Robotics @ 4
OpenCL @ 4
Performance Analysis @ 4
LLVM @ 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 Compiler (DLC) team builds compiler technology used across data centers, personal devices, automotive, and robotics to deliver leading inference performance, fast build time, reduced memory footprints, and ease of use (Ahead-of-Time and Just-in-Time).
Responsibilities
- Analyze deep learning networks and develop compiler optimization algorithms.
- Collaborate with deep learning software framework teams and hardware architecture teams to accelerate next-generation deep learning software.
- Define public APIs, implement performance optimizations and analysis, and craft compiler infrastructure techniques for neural networks.
- General software engineering work related to the compiler and inference stack.
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 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.
Ways to Stand Out
- Proficient in CPU and/or GPU architecture. CUDA or OpenCL programming experience.
- Experience in systems with constrained resources (embedded platforms, small memory size, cross compilation).
- Experience with MLIR, XLA, TVM, LLVM, deep learning models and algorithms, and deep learning frameworks such as PyTorch.
- Experience with GPU kernel generation focused on high performance and fast build time.
- A track record of mentoring junior engineers and interns is a bonus.
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 provided in original posting).
Additional Information
- Full-time role. Applications for this job will be accepted at least until May 4, 2026.
- NVIDIA uses AI tools in its recruiting processes and is an equal opportunity employer.
#deeplearning