Deep Learning Performance Architect - New College Grad 2027

NVIDIA•Santa Clara, CA

About The Position

NVIDIA is hiring new college graduates to join its GPU compute architecture and performance engineering team. The team focuses on pre-silicon modeling, data-supported architecture exploration, and GPU microarchitecture improvements for LLM inference, agentic AI, and other generative AI workloads. The role helps improve the efficiency and scalability of these models on NVIDIA platforms, with hands-on work in performance modeling, correlation, bottleneck analysis, GPU architecture studies, and system-level analysis. It also contributes to system-level modeling and correlation solutions that support accurate projections and guide performance and efficiency improvements for next-generation models in data center and edge environments. The architect collaborates with engineers across kernel, hardware, and performance teams. The base salary range is $124,000–$195,500 for Level 2 and $152,000–$241,500 for Level 3. The employee is also eligible for equity and benefits. Applications will be accepted at least until October 31, 2026. This posting is for an existing vacancy. NVIDIA uses AI tools in its recruiting processes and is an equal opportunity employer committed to an inclusive work environment.

Requirements

Bachelor’s, master’s, 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 focused on efficiency, such as PyTorch, TensorRT-LLM, Triton, Nsight, or vLLM.
Ability to reason about performance tradeoffs and debug technical issues.
Strong communication skills and ability to work well with others on a team.

Nice To Haves

Experience with CUDA programming, GPU performance analysis, performance modeling, correlation, system-level analysis, or profiling tools.
Hands-on work with performance-sensitive systems, computer architecture, or ML infrastructure through coursework, projects, internships, or research.
Curiosity about how systems work under the hood and a strong drive to solve hard problems.
Motivation to learn quickly, take ownership, and make steady progress on open-ended work.
Ability to work independently, explore unfamiliar technical areas, and follow through on challenging problems.
Personal, academic, or open-source projects related to GPU architecture, performance engineering, ML systems, agentic AI systems, or developer tools.

Responsibilities

Build or improve pre-silicon performance and system-level models to understand workload behavior, architecture tradeoffs, and projection accuracy.
Develop correlation methodologies to compare model projections with lower-level analyses or reference data and improve model fidelity.
Study how GPU microarchitecture and system-level design choices affect end-to-end performance, efficiency, scalability, and projection fidelity.
Identify performance bottlenecks across model execution, kernels, data movement, and system behavior.
Explore and evaluate architecture ideas that improve performance, efficiency, or model fidelity.
Collaborate with kernel, hardware, and framework teams to understand end-to-end behavior for LLMs, agentic AI systems, and other generative AI models.
Use profiling and benchmarking tools as needed to support model development, analysis, and correlation.
Present findings and insights through clear analysis and visualization.