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 research, software, and external hardware partners to shape the next generation of AI systems, from silicon through full-scale deployments.

Our team focuses on understanding and optimizing performance across the full system stack—ensuring that architectural decisions are grounded in rigorous, quantitative analysis of real-world workloads.

About the Role

We are seeking a Performance Modeling Lead to build and lead a small, high-impact team responsible for answering forward-looking architectural questions across AI infrastructure systems.

You will develop modeling frameworks and methodologies to evaluate system-level tradeoffs and guide key design decisions. Your work will directly influence reference architectures, vendor designs, and long-term infrastructure strategy.

This role sits at the intersection of AI workloads, system architecture, and quantitative modeling, and requires strong technical judgment, ownership, and the ability to translate complex analysis into clear, actionable guidance.

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

• Build and own a performance modeling framework/toolchain to evaluate AI systems across multiple levels of abstraction.

• Analyze and quantify architectural tradeoffs across compute, memory, networking, storage, and system topology.

• Develop performance models to guide decisions on:

  • scale-up vs. scale-out architectures

  • interconnect and network design

  • memory hierarchy and system balance.

• Translate modeling outputs into clear recommendations for internal teams and external hardware vendors.

• Influence reference designs and vendor roadmaps through data-driven insights.

• Partner closely with machine learning, systems, and hardware teams to understand workload characteristics and requirements.

• Lead and grow a small team (2–3 engineers), setting technical direction and maintaining high standards for modeling rigor.