Manager, GPU Accelerated Data Analytics

NVIDIA is hiring a Manager, GPU Accelerated Data Analytics in Santa Clara, United States. It pays $224k-$357k 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.
Tools and skills the posting cites include C, CUDA and Python.
Level classifies it as a lead role.
Level lists 2,700+ other open roles at NVIDIA.

AI in this role

Lead a team of performance engineers optimizing complex data analytics and computing workloads on GPU architectures.

ccudapython

performance-engineeringdata-analyticstechnical-leadershipaccelerated-computing

NVIDIA's Developer Technology Engineering team is a global network of world-class experts pushing the boundaries of accelerated computing! We empower developers with groundbreaking solutions while driving innovation that fuels NVIDIA's leadership in this transformative field. NVIDIA's accelerated computing platform is revolutionizing industries. To capitalize on this explosive growth, we're growing our team and seeking a visionary leader to drive our continued success.

What you’ll be doing:

This role offers a unique opportunity to lead a team of skilled performance engineers collaborating directly with the developer community to unlock the full potential of NVIDIA's groundbreaking CPUs and GPUs! NVIDIA platform is known for its AI dominance in deep learning training and inference. Nonetheless, modern data centers have hundreds of millions of CPUs that are used today to run mission critical workloads such as database, data preprocessing, compression, video transcoding, web servers, and many others. Help modernize today’s data centers by researching and developing implementations to help save cost and power using the advanced NVIDIA platform. Core responsibilities include:

Driving Innovation: This involves researching, analyzing, and developing innovative techniques to optimize performance of complex workloads across cloud and on-premise environments. These workloads are difficult to parallelize and major performance speed-ups require inventing new algorithms and working side-by-side with architects to influence hardware.
Technical Leadership: Drive technical excellence by leading software design decisions, influencing architecture roadmap, and effectively communicating technical solutions to multi-functional teams. Prioritize and lead key initiatives that advance the performance and adoption of NVIDIA’s hardware and software platforms.
Growing and Mentoring Your Team: Build a distributed world-class team of performance engineers. Grow domain, hardware and software expertise within your team to strengthen external developer engagements. Foster a collaborative and innovative culture that encourages idea sharing, empowers team members, and provides opportunities for professional growth.
Collaboration and Communication: Collaborate closely with company leadership, research teams, and cross-functional partners to drive strategic decision-making, program management, and successful initiative implementation. Advocate for next-generation hardware and software products that address the evolving needs of the developer community.

What we need to see:

An MS or PhD in Computer Science, Computer Engineering, or in a related computationally focused science degree (or equivalent experience).
7+ overall years of relevant experience with 4+ years in a technical role and 3+ years of experience in an engineering leadership role.
Outstanding leadership, strong cross-functional collaboration, and impactful project execution.
Hands-on experience in low-level performance optimization, including GPU parallel programming, e.g., CUDA.
Programming fluency in C/C++ with a deep understanding of algorithms and software development.
In-depth expertise with CPU and GPU architecture fundamentals.
Strong algorithmic skills and proven experience implementing low-level optimizations for enterprise applications.
A track record of building high-performing teams by attracting and hiring top engineering talent.
Excellent communication and presentation skills.
Demonstrated ability to successfully plan, lead, and execute high-impact initiatives.

Ways to stand out from the crowd:

A PhD in a relevant field is highly valued.
Experience leading engineering teams to design performance-first prototypes.
Strong background in distributed high-performance data analytics including SQL or vector databases.
Expertise in modern data center network and storage technologies.

NVIDIA is widely considered to be one of the technology industry's most desirable employers. We have some of the most forward-thinking and hardworking people in the world working with us. Come join our team of brilliant minds leading the way in accelerated computing. Are you a creative and independent computer scientist with a passion for parallel computing? If so, we invite you to apply.

#LI-Hybrid

Your base salary will be determined based on your location, experience, and the pay of employees in similar positions. The base salary range is 224,000 USD - 356,500 USD for Level 3, and 272,000 USD - 431,250 USD for Level 4.

You will also be eligible for equity and benefits.

Applications for this job will be accepted at least until August 9, 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

Manager, GPU Accelerated Data Analytics at NVIDIA rates 30 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.

Key facts

Manager, GPU Accelerated Data Analytics is an open job at NVIDIA.
Level rates this role AI Level 1 of 4: Little AI.
The listed salary is $224k-$357k a year.
The role is based in US, CA, Santa Clara.
The job was posted on August 5, 2026.
Seniority: lead.
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

Performance EngineeringData AnalyticsTechnical LeadershipAccelerated ComputingCCUDAPython

Questions you could be asked

Tell me about a project where performance engineering was part of your work. What did you do?
Tell me about a project where data analytics was part of your work. What did you do?
Tell me about a project where technical leadership was part of your work. What did you do?
Tell me about a project where accelerated computing was part of your work. What did you do?
Walk me through how you've used C in your day-to-day work.

Adapt your resume

List these exact terms on your resume: Performance Engineering, Data Analytics, Technical Leadership, Accelerated Computing, and C. 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.

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