Deep Learning Performance Architect - New College Grad 2027
$124k - $242k • US, CA, Santa Clara
Posted 2d ago
Job Location
US, CA, Santa Clara
Tech Stack
Remote Work Policy
On-site
Categories
AI Infrastructure Engineer
About the job
NVIDIA is seeking New College Graduates to join our GPU compute architecture and performance engineering team. This role focuses on pre-silicon modeling, data-supported architecture exploration, and GPU microarchitecture improvement for generative AI workloads, including LLM inference and agentic AI. You will contribute to improving the efficiency and scalability of these models on NVIDIA platforms, gaining hands-on experience with performance modeling, bottleneck analysis, and system-level analysis. This is an opportunity to shape the performance of next-generation AI models in data center and edge environments by developing system-level modeling and correlation solutions.
Responsibilities
- Build or improve pre-silicon performance and system-level models to understand workload behavior and architecture tradeoffs.
- Develop correlation methodologies to compare model projections with lower-level analyses.
- Study how GPU microarchitecture and system-level design choices affect end-to-end performance and efficiency.
- Identify performance bottlenecks across model execution, kernels, data movement, and system behavior.
- Explore and evaluate architecture ideas to improve performance and efficiency.
- Collaborate with kernel, hardware, and framework teams to understand end-to-end behavior for AI models.
- Use profiling and benchmarking tools to support model development and analysis.
- Present findings and insights through clear analysis and visualization.
Requirements
- BS, MS, or PhD in Computer Science, Computer Engineering, Electrical Engineering, or a related field, or equivalent experience.
- Good programming skills in C++ and Python, with solid software engineering fundamentals.
- Basic understanding of modern deep learning models such as Transformers or LLMs.
- Interest in pre-silicon modeling, GPU architecture, correlation, system-level behavior, or systems optimization.
- Exposure to computer architecture, parallel programming, performance analysis, correlation, system modeling, or related systems topics through coursework, research, internships, or projects.
- Familiarity with at least one ML framework, modeling environment, profiling tool, or software stack passionate about efficiency such as PyTorch, TensorRT-LLM, Triton, Nsight, vLLM, or similar.
- Ability to reason about performance tradeoffs and debug technical issues.
- Strong communication skills and ability to work well with others on a team.
Benefits
- Equity
- Benefits