Senior AI Training Performance Architect

Nvidia

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Quick summary

Work type: On-site
Location: Santa Clara, CA
Salary: $184,000–$287,500 / yr
Employment: Full-time
Posted: 95 days ago
Freshness: Confirmed live today
Nearby: 99+ roles within 25 mi

Market check

Salary context

Above market

How this pay compares to similar roles

Similar $203k

This role $236k

$148k $302k

below market most similar roles pay here above market

This role pays more than 80% of similar roles. Most pay $170,343–$235,750 — the blue band above. At the midpoint, this role pays about $236k versus about $203k for comparable roles.

Based on 240 similar postings.

Employer

About Nvidia

Nvidia is a leading designer of graphics processing units (GPUs) and system-on-chip units, powering gaming, professional visualization, data centers, and artificial intelligence workloads. Industry: Semiconductors & AI Computing

Nvidia currently has 1623 open roles on FindRole.

Listed pay typically runs $184,000–$287,500 across 1110 roles with salary data.

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At a glance

TL;DR · Senior AI Training Performance Architect

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The Senior AI Training Performance Architect joins the team to optimize AI training workloads on state-of-the-art hardware and software platforms. The role involves identifying performance bottlenecks, prioritizing solutions across key training workloads, and implementing production-quality software across the deep learning platform stack, from drivers to frameworks. Responsibilities include building and supporting NVIDIA submissions for MLPerf Training benchmarks, implementing training workloads in proprietary processor and system simulators, and developing tools to automate workload analysis and optimization. Candidates must possess a strong background in deep learning, neural networks, and computer architecture fundamentals. Required technical skills include proficiency in C++, Python, and CUDA. This position focuses on the technical challenge of squeezing every clock cycle out of AI training to support the design of powerful compute systems.

Skills

CUDA C++ Python GPU Architecture Deep Learning Neural Networks Performance Modeling MLPerf Training Computer Architecture System Simulators Performance Analysis Drivers DL Frameworks

What you'll do

Analyze, profile, and optimize AI training workloads on state-of-the-art hardware and software platforms.
Identify and solve performance bottlenecks across key AI training workloads on GPUs.
Implement production-quality software across the deep learning platform stack from drivers to frameworks.
Build and support NVIDIA submissions for MLPerf Training benchmarks.
Implement key deep learning training workloads in proprietary processor and system simulators.
Develop tools to automate workload analysis, optimization, and other critical workflows.

What we're looking for

PhD in CS, EE, or CSEE with 5+ years of relevant experience.
MS in CS, EE, or CSEE with 8+ years of relevant experience.
Strong background in deep learning and neural networks, particularly in training.
Solid understanding of computer architecture and familiarity with GPU architecture fundamentals.
Proven background in analyzing and tuning application performance.
Proven experience with processor and system-level performance modeling.
Proficiency in programming with C++, Python, and CUDA.

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