Senior AI Performance Tools Architect

NVIDIA AI; Santa Clara (CA); On-site; USD 184,000 - 357,000; Full time

Benefits offered by this job

Equity

Job summary

NVIDIA AI is seeking a senior software engineer to automate and optimize performance analysis workflows for AI training and inference workloads. You will design and build tools that scale for a broad audience of engineers and integrate profiling across AI frameworks.

Lead the development of scalable tooling, collaborate with hardware and software teams, and identify bottlenecks with automated approaches. Expect a high-impact role in a leading AI optimization group.

Qualifications

• MS or PhD in Computer Science, Computer Engineering, or related field; 6+ years of 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, C++ preferred).
• Translate user requirements into scalable tooling solutions.
• Up to date with AI-enabled tooling for software development and performance analysis.
• Strong interpersonal skills across multi-functional teams.

Responsibilities

• Design and build performance analysis tools and workflows for AI training and inference workloads.
• Translate engineer 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 align profiling with real workloads.
• Identify performance bottlenecks and develop automated detection/diagnosis approaches.

Skills

Python

C++

AI frameworks

Performance profiling

Education

MS/PhD in CS or related

Tools

Profiling tools

Job description

NVIDIA AI is seeking a senior software engineer to automate and optimize performance analysis workflows for AI training and inference workloads. You will design and build tools that scale for a broad audience of engineers and integrate profiling across AI frameworks.

Lead the development of scalable tooling, collaborate with hardware and software teams, and identify bottlenecks with automated approaches. Expect a high-impact role in a leading AI optimization group.