Performance Modeling Engineer ~2

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 support next-generation infrastructure design.

About the Role

We are seeking an Performance Modeling Engineer to support the development and application of modeling tools used to evaluate AI system performance and inform architectural decisions.

In this role, you will partner closely with Senior Performance Modeling Engineers and the Performance Modeling Lead to analyze system behavior, run simulations and analytical models, and help evaluate tradeoffs across compute, memory, networking, and storage. You will contribute to building modeling frameworks while developing a strong foundation in system architecture and AI infrastructure.

This role is ideal for early-career engineers with 1–2 years of experience in software engineering, systems analysis, or performance modeling who are excited to grow in large-scale infrastructure and hardware/software systems.

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

• Support the development and maintenance of performance modeling tools and frameworks

• Assist in building models to evaluate system behavior across compute, memory, networking, and interconnect subsystems

• Help analyze distributed system scaling behavior and identify performance bottlenecks

• Run simulations and analytical models to support architecture and infrastructure decisions

• Partner with senior engineers to evaluate design tradeoffs across hardware and system components

• Interpret modeling outputs and help translate findings into clear recommendations

• Validate models using benchmarking data and real system performance measurements

• Improve modeling workflows, documentation, and usability for broader team adoption

• Collaborate cross-functionally with hardware, infrastructure, and architecture teams

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