Primary record

Performance Modeling Engineer

OpenAI Indexed employerSan Francisco · Seattle
Source-hosted applyChecked 4h ago$293K–$385K/yrFull-Time
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Workplace

hybrid

Employment

Full-Time

Published

Apr 20, 2026

Closes

No date supplied

The role

About the Team OpenAI’s Hardware organization develops system and infrastructure solutions designed for the unique demands of advanced AI workloads. We work closely with architecture, infrastructure, and vendor teams to evaluate system performance and guide critical design decisions. Our team focuses on building and applying performance modeling frameworks to understand system behavior, quantify tradeoffs, and inform next-generation infrastructure design. About the Role We are seeking Performance Modeling Engineers to develop and apply modeling tools that evaluate AI system performance and inform architectural decisions. In this role, you will work closely with the Performance Modeling Lead and partner teams to analyze system behavior, run simulations or analytical models, and help quantify tradeoffs across compute, memory, networking, and storage. You will contribute to building modeling frameworks and applying them to real-world questions that impact system design and vendor decisions. This role is well-suited for engineers with strong software or modeling backgrounds who are interested in developing deeper expertise in system architecture and AI infrastructure. This role is based in San Francisco, CA. We use a hybrid work model of 3 days in the office per week and offer relocation assistance. Key Responsibilities - Develop and maintain performance modeling tools and frameworks. - Build models to evaluate system behavior across: - compute, memory, and interconnect subsystems - distributed system scaling and bottlenecks. - Run simulations and analytical models to support architectural tradeoff analysis. - Collaborate with performance modeling lead and system architects to answer forward-looking design questions. - Analyze and interpret modeling outputs, translating results into actionable insights. - Validate models against real system measurements and workload behavior. - Contribute to improving modeling fidelity, usability, and scalability. Qualifications - Stron

Requirements

Department: Scaling; Team: Compute