Performance Modeling Engineer

90 billion by 2025, professionals with expertise in AI system performance and infrastructure design are poised for significant career growth. Openai, a pioneer in AI research and deployment, offers a compelling environment for professionals looking to make a mark in this field. Before applying, candidates should be prepared to demonstrate their technical prowess and collaborative mindset.

## About the Role The Performance Modeling Engineer role at Openai is a pivotal position within the company's Hardware organization, which is dedicated to developing system and infrastructure solutions tailored to the unique demands of advanced AI workloads. This team plays a crucial role in evaluating system performance, guiding critical design decisions, and informing the development of next-generation infrastructure. The role involves working closely with the Performance Modeling Lead and other partner teams to analyze system behavior, develop and apply modeling tools, and quantify tradeoffs across various system components.

The performance modeling team is central to Openai's mission of advancing AI capabilities while ensuring safety and efficiency. By focusing on the development and application of performance modeling frameworks, the team contributes to understanding system behavior, identifying performance bottlenecks, and optimizing system design. This work is essential for the development of AI systems that can meet the complex demands of modern applications.

As part of this team, the Performance Modeling Engineer will be at the forefront of shaping the future of AI infrastructure. The role offers a unique opportunity to combine technical expertise with the vision of creating impactful AI solutions. The ideal candidate will have a strong background in software engineering or modeling, familiarity with system architecture fundamentals, and a proven ability to collaborate across teams.

## What You Will Do - Develop and maintain performance modeling tools and frameworks to evaluate AI system performance.

• Build models to analyze system behavior across compute, memory, and interconnect subsystems, as well as distributed system scaling and bottlenecks.
• Run simulations and analytical models to support architectural tradeoff analysis and guide system design decisions.
• Collaborate with the performance modeling lead, system architects, and other stakeholders to answer forward-looking design questions and inform next-generation infrastructure development.
• Analyze and interpret modeling outputs, translating results into actionable insights for system optimization.
• Validate models against real system measurements and workload behavior to ensure modeling accuracy and relevance.
• Contribute to improving modeling fidelity, usability, and scalability to enhance the team's capabilities and efficiency.
• Engage with cross-functional teams, including architecture, infrastructure, and vendor teams, to integrate performance modeling insights into the design process.
• Stay updated with the latest developments in AI, system architecture, and performance modeling to continuously improve the team's tools and methodologies.

## What We Are Looking For - Strong software engineering or modeling background, with experience in simulation, systems modeling, or performance analysis.

• Familiarity with system architecture fundamentals, including compute, memory, networking, and interconnects.
• Experience with programming languages and building technical tools or frameworks.
• Ability to reason about performance bottlenecks and scaling behavior in complex systems.
• Strong analytical skills, with comfort working with quantitative models and data analysis.
• Ability to collaborate across teams, learn new system domains quickly, and adapt to evolving project requirements.
• Experience with AI/ML workloads or distributed systems is a plus, though not required.
• Familiarity with data center infrastructure, large-scale systems, or performance modeling is desirable.
• Interest in system architecture and hardware/software co-design, with a passion for optimizing system performance.

## Nice to Have - Exposure to AI/ML workloads, distributed systems, or high-performance computing environments.

• Experience with simulation tools, performance modeling, or systems analysis in a professional or academic setting.
• Familiarity with data center infrastructure, cloud computing, or large-scale system deployments.
• Knowledge of programming languages relevant to performance modeling and system analysis, such as Python, C++, or MATLAB.
• Participation in open-source projects or personal initiatives related to system performance, AI, or related fields.

## Benefits and Perks - The opportunity to work with a pioneering AI research and deployment company, contributing to the development of cutting-edge AI systems.

• Collaborative and dynamic work environment with a team of experienced professionals in the field of AI and system architecture.
• Access to cutting-edge technologies and tools for performance modeling and system analysis.
• Professional development opportunities, including training, workshops, and conferences related to AI, system architecture, and performance modeling.
• Competitive compensation package, including salary, benefits, and equity, tailored to attract top talent in the field.
• Flexible work arrangements, with a hybrid model allowing for both in-office collaboration and remote work flexibility.
• Relocation assistance for candidates moving to San Francisco, facilitating a smooth transition to the new role.
• Comprehensive health insurance, retirement plans, and other benefits to support the well-being and financial security of employees.

- tip: To stand out in your application, prepare examples of your work in performance modeling, including any personal projects or contributions to open-source initiatives that demonstrate your skills and passion for the field.

• tip: Highlight your ability to collaborate across teams and disciplines, as this role requires close interaction with system architects, engineers, and other stakeholders to inform design decisions and optimize system performance.
• tip: Be prepared to discuss your understanding of system architecture fundamentals, including how different components (compute, memory, networking) impact overall system performance and how you approach modeling and analyzing these systems.
• tip: Demonstrate your analytical skills by discussing how you approach data analysis, model validation, and the interpretation of results in the context of system performance and optimization.
• tip: Show a keen interest in the latest developments in AI, system architecture, and performance modeling, and be prepared to discuss how you stay updated with industry trends and advancements.
• tip: Prepare questions for the interview, such as what the typical workflow and collaborations look like, opportunities for professional growth, and how the company approaches innovation and experimentation in AI and system performance.