Why This Job is Featured on The SaaS Jobs
This Performance Modeling Engineer role sits at an increasingly important intersection for SaaS: the infrastructure layer that determines whether AI-enabled products can deliver predictable latency, throughput, and cost at scale. While the work is framed around hardware and systems, the output influences the reliability envelope that modern SaaS platforms depend on, especially as AI workloads become part of production user experiences.
From a SaaS career perspective, the role builds durable leverage in performance thinking: turning ambiguous workload behavior into quantitative models, validating assumptions against real measurements, and translating findings into decisions that shape roadmaps. That combination of simulation, benchmarking, and tradeoff analysis maps well to recurring SaaS challenges such as capacity planning, multi-tenant efficiency, and meeting SLOs under growth. The cross-functional nature of the work also mirrors how SaaS organizations align engineering, infrastructure, and external vendors around shared constraints.
This position tends to suit engineers who prefer analytical problem framing over feature delivery, and who enjoy building tools that other teams rely on. It is a strong match for those who want to deepen systems intuition while staying close to product-impacting infrastructure decisions, particularly in environments where AI workload characteristics drive platform design.
The section above is editorial commentary from The SaaS Jobs, provided to help SaaS professionals understand the role in a broader industry context.
Job Description
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 inform next-generation infrastructure design.
About the Role
We are seeking Performance Modeling Engineers to develop and apply modeling tools that evaluate AI system performance and inform architectural decisions.
In this role, you will work closely with the Performance Modeling Lead and partner teams to analyze system behavior, run simulations or analytical models, and help quantify tradeoffs across compute, memory, networking, and storage. You will contribute to building modeling frameworks and applying them to real-world questions that impact system design and vendor decisions.
This role is well-suited for engineers with strong software or modeling backgrounds who are interested in developing deeper expertise in system architecture and AI infrastructure.
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
Develop and maintain performance modeling tools and frameworks.
Build models to evaluate system behavior across:
compute, memory, and interconnect subsystems
distributed system scaling and bottlenecks.
Run simulations and analytical models to support architectural tradeoff analysis.
Collaborate with performance modeling lead and system architects to answer forward-looking design questions.
Analyze and interpret modeling outputs, translating results into actionable insights.
Validate models against real system measurements and workload behavior.
Contribute to improving modeling fidelity, usability, and scalability.
Qualifications
Strong software engineering or modeling background (e.g., simulation, systems modeling, or performance analysis).
Familiarity with system architecture fundamentals (compute, memory, networking).
Experience with programming and building technical tools or frameworks.
Ability to reason about performance bottlenecks and scaling behavior.
Strong analytical skills and comfort working with quantitative models.
Ability to collaborate across teams and learn new system domains quickly.
Preferred Skills
Exposure to AI/ML workloads or distributed systems.
Experience with simulation tools, performance modeling, or systems analysis.
Familiarity with data center infrastructure or large-scale systems.
Experience working with performance data, benchmarking, or profiling tools.
Interest in system architecture and hardware/software co-design.
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 mission, we must encompass and value the many different perspectives, voices, and experiences that form the full spectrum of humanity.
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.
For additional information, please see OpenAI’s Affirmative Action and Equal Employment Opportunity Policy Statement.
Background checks for applicants will be administered in accordance with applicable law, and qualified applicants with arrest or conviction records will be considered for employment consistent with those laws, including the San Francisco Fair Chance Ordinance, the Los Angeles County Fair Chance Ordinance for Employers, and the California Fair Chance Act, for US-based candidates. For unincorporated Los Angeles County workers: we reasonably believe that criminal history may have a direct, adverse and negative relationship with the following job duties, potentially resulting in the withdrawal of a conditional offer of employment: protect computer hardware entrusted to you from theft, loss or damage; return all computer hardware in your possession (including the data contained therein) upon termination of employment or end of assignment; and maintain the confidentiality of proprietary, confidential, and non-public information. In addition, job duties require access to secure and protected information technology systems and related data security obligations.
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