Why This Job is Featured on The SaaS Jobs
This role sits at a critical intersection for modern SaaS: the economics and reliability of AI inference in production. For SaaS products that embed generative models, latency, throughput, and utilization are not abstract infrastructure concerns but direct drivers of user experience and unit cost. The focus on modeling performance across application, model, and fleet layers reflects how mature AI platforms are increasingly managed as end to end services rather than isolated components.
From a SaaS career standpoint, the work builds durable leverage in cost to serve thinking, capacity planning, and performance engineering that translates across subscription businesses. Experience turning microbenchmarks into decision grade estimates is especially relevant in SaaS environments where launch readiness, pricing, and roadmap tradeoffs depend on credible projections. The emphasis on tooling and cross team partnership also mirrors how platform engineering influences product outcomes at scale.
This position fits engineers who prefer analytical problem solving, measurement driven iteration, and working across multiple abstraction layers. It will resonate with professionals who enjoy connecting low level systems behavior to business level constraints, and who want to be close to how AI capabilities are operationalized into dependable SaaS features.
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
Our team analyzes inference stack performance across the application, model, and fleet layers to identify bottlenecks and drive faster, cheaper inference. We combine systems profiling, benchmarking, and analysis to understand where time and cost are spent, then turn that understanding into performance optimizations and models that project performance and capacity needs for future launches.
About the Role
In this role, you will model inference performance across application, model, and fleet layers with higher fidelity. You will build cost-to-serve estimates from microbenchmarks and create tools that help cross-functional teams reason about latency, capacity, utilization, and cost tradeoffs.
In this role, you will:
Build and refine performance models that translate microbenchmark results into cost-to-serve estimates.
Analyze inference workloads end to end across applications, models, and fleet infrastructure.
Enhance tooling to identify bottlenecks across layers for latency and throughput.
Partner with other teams to turn performance insights into concrete improvements and project how future changes affect inference.
You might thrive in this role if you:
Enjoy reasoning from first principles about distributed systems, model inference, and hardware efficiency.
Are comfortable working across abstraction layers, from application behavior to kernels, accelerators, networking, and fleet scheduling.
Have deep expertise with performance profiling, benchmarking, analysis, and optimization.
Enjoy collaborating with engineering and research teams to improve real production systems.
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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