Systems Performance Engineer, Agentic AI Workloads – New College Grad 2026

Posted Yesterday
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3 Locations
In-Office
124K-242K Annually
Entry level
Artificial Intelligence • Computer Vision • Hardware • Robotics • Metaverse
The Role
Develop and extend C++ and Python simulators for agentic AI workloads, run simulations, analyze performance bottlenecks, and collaborate on AI system designs.
Summary Generated by Built In

NVIDIA is looking for a Deep Learning Architect to join our team working at the cutting edge of AI infrastructure. As agentic LLM workloads reshape the demands placed on modern datacenters, we need engineers who can model, simulate, and reason about complex system-level traffic at scale. If you have a passion for performance analysis, a strong quantitative foundation, and excitement about the future of AI systems, we'd love to talk.

In this role, you will build and run simulations that capture the traffic dynamics of agentic AI workloads, mine the results for actionable insights, and help guide architectural decisions for next-generation datacenter and GPU systems.
What you'll be doing:

  • Develop and extend C++ and Python simulators that model system-level network and compute traffic for agentic LLM workloads in datacenter environments

  • Characterize real-world LLM serving workloads and distill them into representative simulator inputs

  • Run simulations at scale and apply statistical techniques to post-process and interpret results

  • Identify performance bottlenecks and translate findings into concrete architectural recommendations

  • Collaborate with hardware, software, and research teams to influence the design of future AI systems

What we need to see:

  • Pursuing or recently completed a MS, or PhD in CS, EE, Mathematics, or a related field (or equivalent experience)

  • Strong programming skills in C++ and Python

  • Solid foundations in queueing theory and traffic modeling (e.g., Erlang models, Little's Law)

  • Strong statistics background: characterize huge datasets with percentiles, distributions, and clustering techniques such as K-means

  • Understanding of deep learning fundamentals, LLMs, and modern inference serving frameworks

Ways to stand out from the crowd:

  • Hands-on experience with traffic or network simulators, even in an academic or course project context

  • Familiarity with roofline modeling and performance scaling of deep learning models at the kernel level

  • Experience running large-scale simulation campaigns and building data pipelines to process and visualize results

  • Prior work characterizing or benchmarking ML inference workloads

NVIDIA is widely considered one of the technology world's most desirable employers. We work on problems that matter — and we do it with some of the most talented engineers on the planet. If you're analytically sharp, intellectually curious, and ready to have real impact, we want to hear from you.

Your base salary will be determined based on your location, experience, and the pay of employees in similar positions. The base salary range is 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 June 7, 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.

Skills Required

  • Pursuing or recently completed a MS or PhD in Computer Science, Electrical Engineering, Mathematics, or related field
  • Strong programming skills in C++ and Python
  • Solid foundations in queueing theory and traffic modeling
  • Strong statistics background
  • Understanding of deep learning fundamentals and LLMs

NVIDIA Compensation & Benefits Highlights

The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about NVIDIA and has not been reviewed or approved by NVIDIA.

  • Equity Value & Accessibility Equity awards and a discounted ESPP are highlighted as core parts of total compensation, enabling employees to share in the company’s success. Stock-based compensation and the two-year lookback ESPP are consistently described as especially valuable.
  • Healthcare Strength Health coverage is portrayed as robust, with comprehensive medical, dental, and vision options alongside mental health support and on-site care resources. Employer HSA contributions and wellness perks reinforce the depth of the offering.
  • Retirement Support Retirement programs are depicted as strong, featuring a meaningful 401(k) match with Roth options and support for Mega Backdoor Roth contributions. These elements position long-term savings as a notable advantage of the total rewards package.

NVIDIA Insights

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The Company
HQ: Santa Clara, CA
21,960 Employees
Year Founded: 1993

What We Do

NVIDIA’s invention of the GPU in 1999 sparked the growth of the PC gaming market, redefined modern computer graphics, and revolutionized parallel computing. More recently, GPU deep learning ignited modern AI — the next era of computing — with the GPU acting as the brain of computers, robots, and self-driving cars that can perceive and understand the world. Today, NVIDIA is increasingly known as “the AI computing company.”

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