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NVIDIA

Designs the chips and software that power AI

Distinguished Engineer - End-to-End Scaling Performance Architecture

Hardware EngineeringConfirmed live in the last 24 hours
Full-timeIn-person

About the Job

Requirements

  • MSEE, MSCE, PhD, or equivalent experience in Electrical Engineering, Computer Engineering, Computer Science, or a related field.
  • 18+ years of relevant industry or academic experience, including experience setting architecture direction for complex, high-performance systems.
  • Deep understanding of system performance and scaling, including interactions among DRAM behavior, high-bandwidth fabrics such as NVLink, and C2C communication.
  • Strong application-level intuition, including the ability to connect workload algorithms, parallelism, communication, locality, and data movement to architecture choices and measurable outcomes.
  • Experience with workload characterization, analytical or simulation-based performance modeling, bottleneck analysis, and architecture trade-off evaluation.
  • Demonstrated ability to create and advance a multi-generation technical strategy through influence across silicon, systems, software, and application teams.
  • Clear communication and sound judgment in ambiguous technical areas, with the ability to explain complex system trade-offs to specialists and executive leaders.
  • Experience mentoring senior engineers into broader architecture leadership roles and building strong technical communities.

Responsibilities

  • Define the multi-generation strategy for application scaling across DRAM, NVLink, C2C, compute, and the supporting software stack.
  • Translate the behavior of important AI, HPC, and accelerated computing applications into architectural requirements, performance targets, and investment priorities.
  • Build a clear view of how bottlenecks shift as workloads scale across dies, GPUs, nodes, model sizes, data sets, and communication patterns.
  • Evaluate system-level trade-offs across bandwidth, latency, capacity, topology, coherence, power, area, cost, programmability, and resiliency.
  • Establish common workload scenarios, scaling metrics, models, and decision frameworks so architecture teams can compare proposals against application outcomes.
  • Identify architectural discontinuities and emerging technology opportunities early enough to shape product and technology decisions.
  • Align DRAM, NVLink, C2C, GPU, CPU, system, and software architects around shared performance limits and high-value opportunities.
  • Work with application, framework, compiler, runtime, modeling, and post-silicon teams to connect measured behavior with future architecture choices.
  • Provide clear recommendations to senior technical and business leaders, including assumptions, sensitivities, risks, and expected impact.
  • Mentor system performance architects, strengthen technical communities across teams, generate sustained intellectual property, and help influence the direction of large-scale accelerated computing.

Desired Qualifications

  • Shaped product or technology roadmaps around application-level scaling needs, helped architecture teams build a quantitative understanding of bottlenecks across memory, interconnect, compute, and software, or identified high-impact trade-offs early enough to guide product, architecture, or technology investment.
  • Delivered measurable end-to-end improvements in performance, efficiency, or scaling for priority applications.

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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