Senior Performance Engineer

NVIDIA is hiring a Senior Performance Engineer in Santa Clara, United States. It pays $136k-$213k a year and Level rates it ; you can apply on NVIDIA.

Level's read on this job

On Level's scale, this role is Little AI ●○○○: aI is not part of the work.
Level classifies it as a senior role.
Level lists 2,700+ other open roles at NVIDIA.

AI in this role

As a Performance Engineer at NVIDIA, you will be part of a team that’s advancing the limits of what technology can achieve. This is an outstanding opportunity to work at the forefront of AI, accelerated computing, and modern graphics technologies. You will play a crucial role in crafting the future of our products and influencing key business decisions. If you’re passionate about performance analysis and eager to contribute to world-class innovations, this is the perfect role for you!

What you’ll be doing:

Work directly with Marketing, Architecture, and Engineering across Enterprise business units to provide competitive and internal data analysis. Your insights will set industry performance expectations, support new product initiatives, and influence business decisions.
Bring-up, maintain, and debug pre-production, reference, and consumer hardware to help build the next great NVIDIA product.
Run performance, image quality, and power tests of Professional Visualization, AI, and LLM benchmark applications on various GPUs
Develop and maintain Python-based automation scripts and tools to streamline benchmarking procedures and balance configuration files.
Design new tools and create efficient processes to improve benchmarking analysis and reporting workflows
Analyze and validate results from test runs and craft reports that will be used by various groups within the company.

What we need to see:

Bachelor’s degree (or equivalent experience) with 5+ years of experience in a technical or business marketing field.
Knowledge of Enterprise Workstation/AI/LLM application benchmarking fundamentals, performance analysis, and competitive data comparisons.
Deep understanding in the field 3D application performance testing methods and techniques.
Excellent programming and debugging skills in a scripting language; Python preferred.
Expert knowledge of hardware & PC components to identify and root-cause performance bottlenecks.
Strong verbal and written interpersonal skills.
Demonstrated ability to balance multiple simultaneous projects successfully.

Ways to stand out from the crowd:

Strong skills or previous experience in benchmarking and competitive analysis.
Experience with LLM development and deployment.
Knowledge of NVIDIA hardware and NVIDIA software products.
Strong collaboration and interpersonal skills.

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:
136,000 USD – 212,750 USD for Level 3; and 168,000 USD – 270,250 USD for Level 4.

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

Senior Performance Engineer at NVIDIA rates 19 out of 100 for how much of the daily work is AI. That makes it Little AI (AI Level 1 of 4). The level is about AI in the job, not seniority.

Classification

Little AI. AI is not part of the work.

●●●● 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.