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 @ 5
Algorithms @ 3
Machine Learning @ 3
Communication @ 3
LLM @ 3
PyTorch @ 3
CUDA @ 3
GPU @ 3
Deep Learning @ 3
AI @ 3
Performance Analysis @ 3
- 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
We are looking for a Deep Learning Computer Architect to help design hardware accelerator and processor architectures that enable state-of-the-art machine learning and data analytics algorithms and applications on next-generation mobile, embedded and datacenter platforms. This position offers the opportunity to have real impact in a dynamic, technology-focused company.
Responsibilities
- Contribute to features that help next-generation GPUs advance the state of AI.
- Keep up with the latest deep learning research and collaborate with diverse teams, including deep learning researchers, hardware architects, and software engineers.
- Analyze the behavior of various deep learning methods, propose new features to accelerate or enable those methods, and study the benefits of the proposed features.
Requirements
- MS or PhD degree in computer science, computer architecture, electrical engineering or a related field, or equivalent experience.
- 2+ years of relevant experience in some of the following areas:
- Computer architecture, including GPU and system-level architecture
- Performance analysis and optimization
- Experience with LLM workloads, including performance tuning considerations such as parallelization and fusion strategies
- Experience with core deep learning kernels such as matrix multiply, attention, convolution, and communication
- Programming fluency in C++ and ideally Python.
- Experience with GPU computing (CUDA).
- Experience with deep learning frameworks such as PyTorch.
Compensation
- Base salary range (Level 2): 124,000 USD - 195,500 USD per year.
- Base salary range (Level 3): 152,000 USD - 241,500 USD per year.
- You will also be eligible for equity and benefits.
Additional Information
- Work model: Hybrid (#LI-Hybrid).
- Applications for this job will be accepted at least until June 5, 2026.
- NVIDIA uses AI tools in its recruiting processes and is an equal opportunity employer committed to an inclusive work environment.