OpenAI· Hardware· San Francisco and Seattle
Performance Modeling Lead
Comp$342K – $555K
Classified Tasks (17)
Automate 0%Augment 59%Human-Only 41%
Augment (10)
AI assists, human decides
Build and own a performance modeling framework and toolchain to evaluate AI systems across multiple levels of abstraction
technical
Develop analytical and simulation-based performance models to guide decisions on scale-up vs. scale-out architectures
analytical
Develop analytical and simulation-based performance models to guide interconnect and network design
analytical
Develop analytical and simulation-based performance models to guide memory hierarchy and system balance
analytical
Analyze and quantify architectural tradeoffs across compute, memory, networking, storage, and system topology
analytical
Translate modeling outputs into clear, actionable recommendations for internal teams
communication
Validate and continuously improve modeling fidelity by comparing models against real system behavior and measurements
analytical
Answer forward-looking architectural questions across AI infrastructure systems with quantitative analysis
analytical
Evaluate system-level performance implications of changes in topology, resource allocation, and component selection
analytical
Produce and present clear reports and recommendations that translate complex analysis into actionable guidance for stakeholders
communication
Human-Only (7)
Requires human judgment
Translate modeling outputs into clear, actionable recommendations for external hardware vendors
communication
Influence reference designs and vendor roadmaps through data-driven analyses and insights
leadership
Partner closely with machine learning, systems, and hardware teams to characterize workload behavior and requirements
operational
Lead and grow a small performance modeling team of 2–3 engineers, setting technical direction and priorities
leadership
Set and maintain high standards for modeling rigor, methodologies, and documentation
leadership
Shape long-term infrastructure strategy by providing modeled projections and tradeoff analyses
leadership
Coordinate with research, software, and external hardware partners to align modeling efforts with system and product roadmaps
operational
Job description
Performance Modeling Lead | OpenAI Careers ## Performance Modeling Lead Hardware - San Francisco and Seattle Apply now(opens in a new window) **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. * Continuously improve modeling fidelity by validating against real system behavior and measurements. **Qualifications** * Have experience owning or building performance modeling frameworks used to drive real system design decisions. * Have deep knowledge of AI/ML workloads, including training and/or inference at scale. * Understand system-level tradeoffs across compute, memory, and networking in large-scale distributed systems. * Are comfortable working across abstraction layers—from workload behavior to hardware implementation. * Have experience using modeling (analytical or simulation) to inform architectural decisions. * Can operate in ambiguous problem spaces and turn open-ended questions into structured analysis. * Communicate clearly and influence both internal teams and external partners. **Preferred Skills** * Experience working with hardware vendors (ODM/JDM, silicon, networking). * Background in data center infrastructure or hyperscale systems. * Familiarity with accelerators (GPUs/ASICs) and interconnects (e.g., NVLink, InfiniBand, Ethernet). * Experience influencing hardware roadmaps or reference architectures. * Prior experience leading or mentoring engineers. **About OpenAI** OpenAI is an AI research and deployment company dedicated to ensuring that general-purpose artificial intelligence benefits all of humanity. We push the boundaries of the capabilities of AI systems and seek to safely deploy them to the world through our products. AI is an extremely powerful tool that must be created with safety and human needs at its core, and to achieve our m