NVIDIA

2 weeks ago

Senior GPU Architect, Deep Learning

✓ Verified Job

Canonical indexed version, validated from employer's careers page.

Source: Company careers pageValidated by: Fursa AI

Last checked: 2 hours agoStatus: Live

Job Summary

Define and architect new GPU hardware features for deep learning and parallel processing, driving microarchitectural exploration across compute pipelines, memory hierarchy, data movement, synchronization, and efficiency. Translate workload bottlenecks into architectural requirements, evaluate performance/power/area tradeoffs, and use functional and performance models to refine designs before implementation. Partner with ASIC, verification, compiler, and software teams to take concepts through modeling, validation, and silicon readiness.

Location: Tel Aviv

Workplace: Onsite

Employment Type: Full time

Job Function: Data Science & Machine Learning

Seniority: Mid level

Key Responsibilities

•Define and architect new GPU hardware features for future deep learning and parallel processing workloads.
•Drive microarchitectural exploration across compute pipelines, memory hierarchy, data movement, synchronization, and performance efficiency.
•Analyze workload behavior and translate bottlenecks into architectural requirements and hardware feature proposals.
•Evaluate performance, power, area, complexity, and programmability tradeoffs for new architectural directions.
•Partner with RTL, design, verification, compiler, and software teams to move features from architecture definition through productization, including validation plans and success criteria.

Key Requirements

•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 parallel processing architectures.
•Strong background in hardware architecture and microarchitecture.
•Experience defining and evaluating architectural features, including performance/power/area tradeoffs.
•Strong programming and scripting skills in C, C++, and Python, with experience in architectural modeling, simulation, or performance analysis.

Experience:12+ yearsGPU architectureComputer architectureParallel computingDeep learning acceleration

Education:

Skills:CommunicationDriving technical workAutonomyDeep technical problem-solving

Tech Stack:CC++PythonRTLSimulationPerformance analysisFunctional modelingSiliconCompiler

Company Brief

NVIDIA

Designs and manufactures GPUs, AI accelerators, and system-on-chip products for gaming, data centers, professional visualization, and automotive markets, enabling advanced graphics, AI, and high-performance computing solutions worldwide.

Industry: Electronics Manufacturing

Company Size: Enterprise (1,001+ employees)

Revenue: USD 1B+

Growth: Public Company

Valuation: Public Company (Market Cap in USD)

Funding: IPO / Publicly Listed

Headquarters: Santa Clara, United States

Founded: 1993

Glassdoor: 4.3

Website LinkedIn Glassdoor

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NVIDIA

2 weeks ago

Senior GPU Architect, Deep Learning

✓ Verified Job

Canonical indexed version, validated from employer's careers page.

Source: Company careers pageValidated by: Fursa AI

Last checked: 2 hours agoStatus: Live

Job Summary

Location: Tel Aviv

Workplace: Onsite

Employment Type: Full time

Job Function: Data Science & Machine Learning

Seniority: Mid level

Key Responsibilities

•Define and architect new GPU hardware features for future deep learning and parallel processing workloads.
•Drive microarchitectural exploration across compute pipelines, memory hierarchy, data movement, synchronization, and performance efficiency.
•Analyze workload behavior and translate bottlenecks into architectural requirements and hardware feature proposals.
•Evaluate performance, power, area, complexity, and programmability tradeoffs for new architectural directions.
•Partner with RTL, design, verification, compiler, and software teams to move features from architecture definition through productization, including validation plans and success criteria.

Key Requirements

•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 parallel processing architectures.
•Strong background in hardware architecture and microarchitecture.
•Experience defining and evaluating architectural features, including performance/power/area tradeoffs.
•Strong programming and scripting skills in C, C++, and Python, with experience in architectural modeling, simulation, or performance analysis.

Experience:12+ yearsGPU architectureComputer architectureParallel computingDeep learning acceleration

Education:

Skills:CommunicationDriving technical workAutonomyDeep technical problem-solving

Tech Stack:CC++PythonRTLSimulationPerformance analysisFunctional modelingSiliconCompiler

Company Brief

NVIDIA

Industry: Electronics Manufacturing

Company Size: Enterprise (1,001+ employees)

Revenue: USD 1B+

Growth: Public Company

Valuation: Public Company (Market Cap in USD)

Funding: IPO / Publicly Listed

Headquarters: Santa Clara, United States

Founded: 1993

Glassdoor: 4.3

Website LinkedIn Glassdoor