Senior Software Engineer, AI Performance Analysis

NVIDIA•Santa Clara, CA

1d•$184,000 - $356,500

About The Position

NVIDIA GPU Architecture Group is seeking a senior software engineer to automate and optimize performance analysis workflows for AI training and inference workloads. The role involves performing performance analysis and reshaping how it is done by building tools and workflows that scale across a broad audience of engineers. The work will advance AI workload optimization. The engineer will design performance analysis tools and workflows, develop integrations between profiling infrastructure and AI frameworks, and work with performance engineers, hardware architects, and software teams. They will identify bottlenecks in AI workloads and develop automated ways to detect and diagnose them. The role is listed at Level 4 or Level 5. Base salary depends on location, experience, and the pay of employees in similar positions; the stated ranges are $184,000–$287,500 for Level 4 and $224,000–$356,500 for Level 5. The position is eligible for equity and benefits. Applications will be accepted at least until October 27, 2026. The posting is for an existing vacancy. NVIDIA uses AI tools in its recruiting processes and states that it is an equal opportunity employer committed to an inclusive work environment.

Requirements

M.S. or Ph.D. in Computer Science, Computer Engineering, or a related field, or equivalent experience.
At least 6 years of relevant work experience.
Deep knowledge of AI workloads, frameworks, and performance characteristics.
Experience building tools, workflows, or infrastructure used by other engineers.
Strong software development skills; Python and C++ are preferred.
Ability to translate user requirements into scalable tooling solutions.
Up-to-date knowledge of AI-enabled tooling for software development and performance analysis.
Strong interpersonal skills for understanding engineer difficulties and working across multifunctional teams.

Nice To Haves

Experience profiling or optimizing AI training or inference pipelines at scale.
Background building developer tools or platforms for ML engineers.
Contributions to open-source AI tooling or frameworks.

Responsibilities

Design and build performance analysis tools and workflows for AI training and inference workloads.
Understand how AI performance engineers work and translate their needs into scalable, intuitive tooling.
Develop integrations between profiling infrastructure and AI frameworks and workflows.
Collaborate with performance engineers, hardware architects, and software teams to ensure profiling capabilities align with real-world AI workloads.
Identify performance bottlenecks in AI workloads and develop automated approaches to detect and diagnose them.