Performance Modeling Lead

About the Role

The Performance Modeling Lead will work closely with machine learning, systems, and hardware teams to understand workload characteristics and requirements, ensuring that architectural decisions are grounded in rigorous, quantitative analysis of real-world workloads. This collaborative approach will enable the team to develop modeling frameworks and methodologies that can be applied across various AI infrastructure systems, ultimately influencing reference architectures, vendor designs, and long-term infrastructure strategy.

What You Will Do

• 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, and 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.
• Collaborate with cross-functional teams to ensure seamless integration of performance modeling into the overall system design process.
• Stay up-to-date with industry trends and advancements in AI, ML, and system architecture, applying this knowledge to enhance the performance modeling framework.

What We Are Looking For

• Experience owning or building performance modeling frameworks used to drive real system design decisions.
• Deep knowledge of AI/ML workloads, including training and/or inference at scale.
• Understanding of system-level tradeoffs across compute, memory, and networking in large-scale distributed systems.
• Ability to work across abstraction layers—from workload behavior to hardware implementation.
• Experience using modeling (analytical or simulation) to inform architectural decisions.
• Strong communication skills, with the ability to operate in ambiguous problem spaces and turn open-ended questions into structured analysis.
• Ability to communicate clearly and influence both internal teams and external partners.
• Strong technical judgment and ownership, with a focus on delivering high-quality results in a fast-paced environment.
• Experience with hardware vendors (ODM/JDM, silicon, networking) is a plus.

Nice to Have

• 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.

Benefits and Perks

• Competitive compensation package, including salary and equity.
• Opportunity to work with a pioneering AI research company.
• Collaborative, dynamic work environment with a team of experienced professionals.
• Flexible remote work arrangements, with a hybrid model of 3 days in the office per week.
• Relocation assistance provided for eligible candidates.
• Access to cutting-edge technologies and tools.
• Professional development opportunities, including training and conference sponsorships.
• Comprehensive health benefits, including medical, dental, and vision coverage.
• Generous PTO policy, with paid holidays and vacation time.

• Develop a strong portfolio showcasing your experience in performance modeling, including any relevant projects or frameworks you've built.
• Highlight your understanding of AI/ML workloads and system architecture, and be prepared to discuss how you've applied this knowledge in previous roles.
• Familiarize yourself with Openai's products and research, and be prepared to discuss how your skills and experience align with the company's goals.
• Practice communicating complex technical concepts to non-technical stakeholders, as this will be an essential skill in this role.
• Be prepared to discuss your experience with modeling tools and frameworks, and how you've used these to inform architectural decisions.
• Research the current market rate for performance modeling leads, and be prepared to negotiate your salary based on your experience and qualifications.
• Ask about the team's dynamics and the company culture during the interview, to ensure you're a good fit for the role and the organization.