Why This Job is Featured on The SaaS Jobs
This Applied AI Engineer role sits at a distinctly SaaS-relevant intersection: taking frontier model capability and turning it into a dependable, user-facing product. The work described is anchored in production constraints that define real AI SaaS offerings, including latency, reliability, cost, and capacity, alongside the core execution loop and interfaces that make an agent usable beyond demos.
For a long-term SaaS career, the role builds durable experience in operationalizing AI systems where outcomes are measured in solve rate, regressions, and real-world usefulness rather than research benchmarks alone. It also exposes the feedback mechanics that increasingly differentiate AI products: instrumentation, evaluation pipelines, and data loops that convert usage into iterative improvements. That combination maps well to modern SaaS environments where model behavior, platform infrastructure, and product experience are tightly coupled.
This position is best suited to professionals who like working across boundaries between research, infrastructure, and product, and who prefer systematic debugging over one-off experimentation. It will fit someone motivated by making complex systems steerable and reliable, and who wants to develop judgment about tradeoffs between quality, performance, and economics in production AI.
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 Core Agent team builds the kernel of Codex. We own making the agent better, accelerating research, and making those improvements real in production for our users.
That means working across the systems that make Codex actually function as an agent in the real world: the production performance envelope around tokens, latency, reliability, cost, and capacity; the core execution loop and interfaces that turn models into useful behavior; the shared infrastructure that enables other teams to build on Codex; and the feedback loops that turn real-world usage into better models and better agent behavior over time.
About the Role
We’re looking for applied AI engineers to help bring Codex agents from impressive demos to dependable tools. This role is about improving agent performance on real software engineering tasks and closing the gap between research capability and real-world usefulness.
You’ll work closely with research, infrastructure, and product to ensure agents are not just powerful, but useful, steerable, and reliable in practice. The job is not only to improve model behavior in isolation, but to turn those improvements into measurable gains in solve rate, usefulness, and economic value for users.
What You’ll Do
Design and iterate on agent behaviors across real-world coding tasks and long-horizon workflows.
Work closely with research to develop and run evals to measure agent performance, regressions, failure modes, and edge cases.
Improve performance through prompting, tool-use strategies, context construction, and model-facing experimentation.
Analyze failures in production and systematically improve robustness and reliability.
Build feedback loops and data systems that get better real-task data into evaluation and research.
Work with product teams to shape user-facing agent experiences and the interfaces the agent depends on.
Help define what “good” looks like for agents completing complex tasks end-to-end.
You Might Be a Good Fit If You
Have experience building or shipping machine learning or LLM-powered products.
Are strong in Python and comfortable with modern ML tooling.
Have worked on model evaluation, fine-tuning, or prompt design.
Think in terms of systems and user outcomes, not just model metrics.
Enjoy debugging messy, real-world failures and turning them into improvements.
Want to work in the layer that turns research and model potential into systems that actually work for users.
Bonus Points
Experience with agent frameworks or tool-using LLM systems.
Research experiencewith code generation models or developer tooling.
Experience working with large, messy datasets or production logs.
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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