Senior High-Performance AI Training Engineer

Nvidia

Quick summary

Work type: On-site
Location: Santa Clara, CA
Salary: $184,000–$287,500 / yr
Posted: 112 days ago
Nearby: 99+ roles within 25 mi

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Salary context

Competitive pay

How this pay compares to similar roles

Similar $215k

This role $236k

$171k most similar roles pay here $300k

This role pays more than 65% of similar roles. Most pay $183,637–$246,150 — the shaded band above. At the midpoint, this role pays about $236k versus about $215k 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 860 open roles on FindRole.

Listed pay typically runs $184,000–$287,500 across 848 roles with salary data.

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View all roles at Nvidia

At a glance

TL;DR · Senior High-Performance AI Training Engineer

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NVIDIA seeks senior engineers passionate about performance analysis and optimization to enhance AI training workloads on cutting-edge hardware and software platforms. In this role, you will analyze and optimize AI model training tasks, pushing end-to-end performance towards physical limits while implementing production-quality software across NVIDIA’s deep learning platform stack, from drivers to frameworks. You’ll also build and support MLPerf Training benchmark submissions and develop tools for automated workload analysis and optimization. Ideal candidates hold a PhD or MS with extensive experience in deep learning, computer architecture, and GPU fundamentals, along with proficiency in C++, Python, and CUDA. This position offers the chance to directly influence hardware and software roadmaps in a leading AI technology company, contributing to the design of the world’s most powerful compute systems.

Skills

Python C++ CUDA MLPerf NVIDIA_Deep_Learning_Platform GPU Computer_Architecture Performance_Modeling CI/CD Docker Kubernetes Terraform AWS Prometheus Grafana

What you'll do

Analyze and optimize AI training workloads on new hardware and software platforms.
Solve performance issues across key AI model training tasks to reach physical limits.
Implement production-quality software in NVIDIA's deep learning platform stack layers.
Build and support submissions for MLPerf Training benchmarks using NVIDIA technology.
Develop tools to automate workload analysis, optimization, and critical workflows.

What we're looking for

PhD in CS, EE or CSEE with 5+ years of relevant experience, or MS with 8+ years.
Strong background in deep learning and neural network training.
Solid understanding of computer architecture and GPU fundamentals.
Proven ability to analyze and tune application performance.
Experience with processor and system-level performance modeling.
Proficiency in C++, Python, and CUDA programming.

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