# Deep Learning Performance Architect - New College Grad 2027 at NVIDIA

NVIDIA is hiring a Deep Learning Performance Architect - New College Grad 2027 in Santa Clara, United States. It pays $124k-$196k a year and Level rates it Builds AI ●●●●; you can [apply on NVIDIA](https://jobsbylevel.com/go/0ffcd001-9a81-4884-b39d-df0ab4d6ec09?utm_medium=ai-agent).

AI Level 4, AI centrality 96 out of 100. US, CA, Santa Clara.

## Details

- Company: [NVIDIA](https://jobsbylevel.com/companies/nvidia)
- AI level: AI Level 4 (score 96 out of 100)
- Location: US, CA, Santa Clara
- Salary: $124k-$196k
- Posted: October 27, 2026
- Apply: https://jobsbylevel.com/go/0ffcd001-9a81-4884-b39d-df0ab4d6ec09?utm_medium=ai-agent
- Free CV review for this job: https://jobsbylevel.com/cv-review?job=deep-learning-performance-architect-new-college-grad-2027-at-nvidia-c5de8b

## Prepare for this job

The job page lists 4 likely interview questions for this offer, with a CV checklist: https://jobsbylevel.com/jobs/deep-learning-performance-architect-new-college-grad-2027-at-nvidia-c5de8b

## Description

NVIDIA is at the forefront of the generative AI revolution. We are looking for New College Graduates to join our GPU compute architecture and performance engineering team, dedicated to pre-silicon modeling, data-supported architecture exploration, and GPU microarchitecture improvement for LLM inference, agentic AI, and other generative AI workloads. As a Deep Learning Performance Architect, you will help improve the efficiency and scalability of large language models (LLMs), agentic AI systems, and other generative AI models on NVIDIA platforms. You will gain hands-on experience with pre-silicon performance modeling, correlation, bottleneck analysis, GPU architecture studies, and system-level analysis, while collaborating with engineers across kernel, hardware, and performance teams. This role offers an opportunity to contribute to real-world studies that help shape the performance of next-generation LLMs, agentic AI systems, and other generative AI models in data center and edge environments. You will help develop the system-level modeling and correlation solutions needed for accurate projections and for guiding performance and efficiency improvements in the agentic era. What you’ll be doing: Build or improve pre-silicon performance and system-level models to better understand workload behavior, architecture tradeoffs, and projection accuracy Develop correlation methodologies to compare model projections with lower-level analyses or available 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 better 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 What we need to see: 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 Ways to stand out from the crowd: 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 exciting problems with personal, academic, or open-source projects related to GPU architecture, performance engineering, ML systems, agentic AI systems, or developer tools Your base salary will be determined based on your location, experience, and the pay of employees in

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