Why This Job is Featured on The SaaS Jobs
This Research Engineer role sits at a point where modern SaaS is being reshaped by AI, specifically through developer-facing products delivered via both an application and an API. The remit spans experimentation through deployment, which is notable in an ecosystem where model capabilities only matter when they can be shipped reliably and operated at scale for real users.
From a SaaS career perspective, the work builds durable strengths in turning research into production systems: evaluation discipline, iteration loops tied to user impact, and an instinct for cost and latency tradeoffs that show up directly in margins for usage-based products. Experience optimizing inference, orchestration, and system performance also transfers well across AI-native SaaS, platform engineering, and product infrastructure roles where reliability and efficiency are core differentiators.
The role is best suited to engineers who enjoy ambiguity and can move between scientific thinking and pragmatic engineering delivery. It will appeal to professionals who like collaborating across research, product, and infrastructure boundaries, and who are motivated by measuring outcomes rather than shipping features in isolation. Candidates with an interest in the operational realities of deploying frontier models into widely used SaaS surfaces should find the scope aligned.
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
The Codex team is responsible for building state-of-the-art AI systems that can write code, reason about software, and act as intelligent agents for developers and non-developers alike. Our mission is to push the frontier of code generation and agentic reasoning, and deploy these capabilities in real-world products such as ChatGPT and the API, as well as in next-generation tools specifically designed for agentic coding. We operate across research, engineering, product, and infrastructure—owning the full lifecycle of experimentation, deployment, and iteration on novel coding capabilities.
About the Role
As a member of the Codex team, you will advance the capabilities, performance, and reliability of AI coding models through a combination of research, experimentation, and system optimization. You’ll collaborate with world-class researchers and engineers to develop and deploy systems that help millions of users write better code, faster—while also ensuring these systems are efficient, cost-effective, and production-ready.
We’re looking for people who combine deep curiosity, strong technical fundamentals, and a bias toward impact. Whether your strengths lie in ML research, systems engineering, or performance optimization, you’ll play a pivotal role in pushing the state of the art and bringing these advances into the hands of real users.
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 to new employees.
In this role, you might:
Design and run experiments to improve code generation, reasoning, and agentic behavior in Codex models.
Develop research insights into model training, alignment, and evaluation.
Hunt down and address inefficiencies across the Codex system stack—from agent behavior to LLM inference to container orchestration—and land high-leverage performance improvements.
Build tooling to measure, profile, and optimize system performance at scale.
Work across the stack to prototype new capabilities, debug complex issues, and ship improvements to production.
You might thrive in this role if you:
Are excited to explore and push the boundaries of large language models, especially in the domain of software reasoning and code generation.
Have strong software engineering skills and enjoy quickly turning ideas into working prototypes.
Think holistically about performance, balancing speed, cost, and user experience.
Bring creativity and rigor to open-ended research problems and thrive in highly iterative, ambiguous environments.
Have experience operating across both ML systems and cloud infrastructure.
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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