Senior GPU Architect, Deep Learning

NVIDIA

Senior GPU Architect defining GPU architectures for deep learning and parallel processing at NVIDIA. Collaborating with teams to drive architectural features and performance improvements.

Posted 7/5/2026full-timeTel Aviv • 🇮🇱 IsraelSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in GPU architecture and microarchitecture, with a strong ability to analyze performance, power, and area tradeoffs. Proficient in developing architectural specifications and collaborating with cross-functional teams to drive successful productization.

Highest-signal resume keywords

GPU ArchitectureMicroarchitectureC ProgrammingC++ ProgrammingPython Programming

ATS Keywords

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Hard Skills

Architectural ModelingPerformance AnalysisParallel ComputingMemory SystemsDeep Learning Acceleration

Soft Skills

Strong Communication Skills

Industry Keywords

Computer ArchitectureParallel ProcessingWorkload Behavior AnalysisPerformance EfficiencyRTL Design

Tech Stack

Tools & technologies

Python

About the role

Key responsibilities & impact

Define and architect new GPU hardware features for future deep learning and parallel processing workloads.
Drive microarchitectural exploration across key areas such as compute pipelines, memory hierarchy, data movement, synchronization, and performance efficiency.
Analyze workload behavior and translate bottlenecks into clear architectural requirements and hardware feature proposals.
Evaluate performance, power, area, complexity, and programmability tradeoffs for new architectural directions.
Develop and use functional and performance models to study new features and refine the architecture before implementation.
Work closely with RTL, design, verification, compiler, and software teams to ensure successful execution from architecture definition to productization.
Create clear architecture specifications, validation plans, and success criteria for the features you define.
Be ready to learn, dig deep, and work across the full stack when required - from workloads and models to RTL and silicon.

Requirements

What you’ll need

BS, MS, or PhD in Computer Science, Electrical Engineering, Computer Engineering, or equivalent experience.
12+ years of relevant industry experience in GPU architecture, computer architecture, or other parallel processing architectures.
Strong background in hardware architecture and microarchitecture.
Experience defining and evaluating architectural features with solid understanding of performance, power, and area tradeoffs.
Strong programming and scripting skills in C, C++, and Python.
Experience with architectural modeling, simulation, or performance analysis.
Background in parallel computing, memory systems, high performance computing, or deep learning acceleration.
Strong communication skills and the ability to drive technical work across distributed, interdisciplinary teams.

Benefits

Comp & perks

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