About this opportunity
OpenAI lists this Performance Modeling Engineer ~2 opportunity in los angeles, California. Review the employer’s description below for duties, qualifications and application requirements.
Job description
Performance Modeling Engineer ~2
Hardware – San Francisco and Seattle
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 a 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
Continuously build technical depth across AI infrastructure, system architecture, and performance analysis
Qualifications
1–2 years of experience in software engineering, systems modeling, performance analysis, or related technical work
Strong programming skills and experience building technical tools, scripts, or frameworks
Familiarity with system architecture fundamentals such as compute, memory, and networking
Ability to reason about system performance, bottlenecks, and scaling behavior
Strong analytical and problem-solving skills with comfort working in quantitative environments
Ability to learn quickly and work effectively across technical teams
Preferred Skills
Exposure to AI/ML workloads, distributed systems, or large-scale infrastructure
Experience with simulation tools, benchmarking, profiling, or performance analysis
Familiarity with data center systems, server architecture, or hardware platforms
Interest in system architecture and hardware/software co-design
Internship or early professional experience in performance engineering, infrastructure, or systems design
Equal Opportunity
We are an equal opportunity employer, and we do not discriminate on the basis of race, religion, color, national origin, sex, sexual orientation, age, veteran status, disability, genetic information, or other applicable legally protected characteristic.
Compensation
$266,000 – $445,000 + Offers Equity
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Worksite address
los angeles, CA, 90079, US
Who can apply
Review the original listing for work authorization, qualifications and employer requirements.