Deep Learning Performance Architect - New College Grad 2027

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 ; you can apply on NVIDIA.

Level's read on this job

On Level's scale, this role is Builds AI ●●●●: the job is building AI systems.
The posting mentions vLLM, PyTorch and C among its tools and skills.
Level classes it as a junior role.
NVIDIA has 1,800+ other open roles on Level.
Posted within the past week.

AI in this role

Build pre-silicon performance models and optimize GPU architecture for generative AI workloads and LLMs at NVIDIA.

vllmpytorchcpythontransformers

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 other objective job-related factors. The expected base salary ranges for this position are:
124,000 USD – 195,500 USD for Level 2; and 152,000 USD – 241,500 USD for Level 3.

You will also be eligible for equity and benefits.

Applications for this job will be accepted at least until November 3, 2026.

This posting is for an existing vacancy.

NVIDIA uses AI tools in its recruiting processes.

NVIDIA is committed to fostering an inclusive work environment and proud to be an equal opportunity employer. As we highly value diversity in our current and future employees, we do not discriminate (including in our hiring and promotion practices) on the basis of race, religion, color, national origin, gender, gender expression, sexual orientation, age, marital status, veteran status, disability status or any other characteristic protected by law.

How we rate this

Deep Learning Performance Architect - New College Grad 2027 at NVIDIA rates 90 out of 100 for how much of the daily work is AI. That makes it Builds AI (AI Level 4 of 4). The level is about AI in the job, not seniority.

Classification

Builds AI. The job is building AI systems.

●●●● Builds AI80 to 100
●●●○ Works on AI60 to 79
●●○○ Uses AI40 to 59
●○○○ Little AI0 to 39

Levels come from how often the tools, models and workflows of the role are named in the posting itself. Read the description and count the AI tools, models and workflows it names.

Key facts

Deep Learning Performance Architect - New College Grad 2027 is an open job at NVIDIA.
Level rates this role AI Level 4 of 4: Builds AI.
The listed salary is $124k-$196k a year.
The role is based in US, CA, Santa Clara.
The job was posted on October 27, 2026.
Seniority: junior.
Applications are handled by NVIDIA. Apply on NVIDIA

Prepare for this job

Built from this posting and from similar jobs on Level.

Skills and AI tools this role asks for

vLLMPyTorchCPythonTransformers

Questions you could be asked

What's a project where you used vLLM hands-on?
Walk me through how you've used PyTorch in your day-to-day work.
What are the limits of C that you've run into, and how did you work around them?
What's a project where you used Python hands-on?
Walk me through how you've used Transformers in your day-to-day work.

Adapt your resume

List these exact terms on your resume: vLLM, PyTorch, C, Python, and Transformers. An applicant tracking system matches the wording, not the idea.
Attach one line of real, concrete experience to at least one of them. A tool named with nothing behind it rarely survives a human read.
Lead with what you built, trained or shipped. This role is judged on the AI system itself, not the tools around it.

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