Tech Lead, Performance Data Platform for AI Workloads - Equity

NVIDIA Corporation; Santa Clara (CA); On-site; USD 272,000 - 431,000; Full time

Benefits offered by this job

Equity

Benefits

Job summary

NVIDIA Corporation in Santa Clara, CA is seeking a Software Engineer, Technical Lead for the Performance Data Platform. You will define the end-to-end architecture, data model, API contracts, and establish boundaries with data producers and partner teams.

You will stand up services and deployment paths, mentor engineers, and drive early use cases with design partners while ensuring scalable performance across multiple nodes.

Qualifications

• 15+ years of software engineering experience, including technical leadership of a data-intensive platform
• Experience with automated agents or models processing large production data
• Practical knowledge of GPU and machine learning workload performance
• Track record of evolving APIs and data models without breaking users
• Hands-on ability to build and deploy services and tooling and verify results

Responsibilities

• Define the end-to-end architecture: data model, service/API contracts, boundaries with data producers and partner teams
• Build in the early phase: stand up services, APIs and deployment paths
• Explain how automated analysis agents operate within the platform, including queries and cost constraints
• Review performance diagnoses and drive first-use cases with partners, mentor engineers
• Align teams on build choices and trade-offs through clear communication

Skills

Leadership experience

APIs & data models

Clear communication

Mentoring engineers

Education

Bachelor’s degree in CS/CE or equivalent

Tools

C++

Go

Python

SQL

TypeScript

Job description

NVIDIA Corporation in Santa Clara, CA is seeking a Software Engineer, Technical Lead for the Performance Data Platform. You will define the end-to-end architecture, data model, API contracts, and establish boundaries with data producers and partner teams.

You will stand up services and deployment paths, mentor engineers, and drive early use cases with design partners while ensuring scalable performance across multiple nodes.