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NVIDIA

Designs the chips and software that power AI

Manager – GPU Accelerated Data Analytics

Engineering LeadershipUpdated on Oct 31
Full-time

About the Job

Requirements

  • An MS or PhD in Computer Science, Computer Engineering, or a related computationally focused science degree, or equivalent experience.
  • At least 7 years of overall relevant experience, including at least 4 years in a technical role and 3 years in an engineering leadership role.
  • Experience in low-level performance optimization, including GPU parallel programming such as CUDA.
  • Programming fluency in C and C++ with a deep understanding of algorithms and software development.
  • In-depth expertise in 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.
  • Ability to plan, lead, and execute high-impact initiatives.

Responsibilities

  • Research, analyze, and develop innovative techniques to optimize performance of complex workloads across cloud and on-premise environments.
  • Invent algorithms for workloads that are difficult to parallelize and work alongside architects to influence hardware.
  • Drive technical excellence by leading software design decisions, influencing the architecture roadmap, and communicating technical solutions to multifunctional teams.
  • Prioritize and lead key initiatives that advance the performance and adoption of NVIDIA hardware and software platforms.
  • Build a distributed team of performance engineers and grow domain, hardware, and software expertise within the team to strengthen external developer engagements.
  • Foster a collaborative and innovative team culture that encourages idea sharing and supports team members' professional growth.
  • Collaborate with company leadership, research teams, and cross-functional partners on strategic decision-making, program management, and implementation of initiatives.
  • Advocate for next-generation hardware and software products addressing the evolving needs of the developer community.
  • Research and develop implementations to modernize data-center workloads, reducing cost and power consumption using the NVIDIA platform.

Desired Qualifications

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

About the Company

NVIDIA

Designs the chips and software that power AI

NVIDIA designs the chips and software that power artificial intelligence and sells complete data center systems to cloud providers, AI labs, and other businesses, along with GeForce graphics cards for gamers. Its GPUs, chips that perform thousands of calculations at once, became the main hardware for AI after researchers used them to train the AlexNet image-recognition model in 2012. NVIDIA connects many GPUs with its own processors and networking gear into servers and rack-sized computers, and developers program them through its CUDA software. These systems bring in most of its sales, and it also makes chips for cars and robots. Jensen Huang co-founded NVIDIA in 1993, and it is based in Santa Clara, California.

Company size
10,001+
Company stage
IPO
Headquarters
Santa Clara, CA
Founded
1993

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