Codex Deployment Engineer in San Francisco

About the Team The Plugin Developer Platform team builds the APIs, SDKs, and tools that let people extend ChatGPT and Codex. We work on plugins, connectors, the Model Context Protocol (MCP), and interactive apps. We want anyone to be able to turn a useful workflow into a plugin, share it, and have other people use it. A plugin can package instructions and skills with connections to the tools and data it needs. Our work covers plugin creation and publishing, the systems that run plugins across our products, and open standards that developers can build on. About the Role We’re looking for platform-minded engineers who know what it takes to build a platform developers want to use. You’ll work across developer-facing interfaces, APIs, and backend systems. You’ll own features from the first developer conversation through implementation and release. You’ll talk directly with developers, partners, and the open-source community. Their experience will inform the APIs and abstractions you design, the problems you prioritize, and the tradeoffs you make. This role is based in San Francisco. We use a hybrid work model of 3 days in the office per week and offer relocation assistance to new employees. What You’ll Do Design and ship APIs, SDKs, and services that developers use to extend ChatGPT and Codex. Make plugins easier to create, test, publish, update, and share. Improve compatibility and consistency across ChatGPT and Codex, including interactive app experiences. Contribute to MCP and other open standards, bringing practical developer needs into their design. Work with developers and partners to understand recurring problems and improve the platform, tooling, and documentation. Work with Product, Research, Security, and Trust & Safety on permissions, compatibility, and safe, reliable execution. You Might Thrive Here If You Have built software that other developers use. Your experience might include an open-source project, an API or SDK, a developer platform, internal too

About the Team API Agents builds the shared agent harness, tools, and infrastructure that turn OpenAI’s frontier models into systems that can reliably complete real work. We carry the capabilities behind Codex into a much broader set of products and workflows across software engineering, research, finance, healthcare, enterprise operations, and more. Our work spans search and connected context, computer use, memory, delegation and multi-agent coordination, and safe execution. Sitting at the intersection of Research, Codex, infrastructure, and applied product teams, we build reusable agent capabilities that compound across the ecosystem. About the Role We are looking for an experienced backend software engineer to build the core systems behind the next generation of agents. You will design reliable services and abstractions that help agents find the right context, use tools and computers, retain knowledge, coordinate over long-running workflows, and take action safely. The role combines deep backend and infrastructure work with strong product judgment, with opportunities to work across agent runtimes, orchestration, search, execution environments, identity and permissions, observability, and evaluations. This is software and systems engineering rather than model training: success comes from strong backend fundamentals, high agency, and the ability to turn fast-moving research capabilities into dependable production primitives. In this role, you will: Design, build, and operate the shared agent harness and backend infrastructure that power long-running, high-value workflows across OpenAI and third-party products. Build reusable capabilities across search and connected context, computer use, memory, tool execution, delegation, subagents, and multi-agent orchestration. Establish the foundations agents need to operate safely in production, including secure execution environments, identity and permissions, observability, evaluations, reliability, and cost and latency effi

About the Team OpenAI’s Education team is building products that advance how people learn with AI. The team works across higher education institutions, K-12 districts, and country-level partnerships, including applied research on how AI affects learning and cognitive outcomes. The team owns owns ChatGPT Edu, ChatGPT for Teachers, and related product/research work. The team partners closely with go-to-market, research, Consumer Learning, and model teams to turn education-specific insights into product experiences that can improve ChatGPT more broadly. Some of our recent work: New Education Plugins for ChatGPT Work and Codex New tools for understanding AI and learning outcomes Education for countries Advancements in higher education Early product work - Introducing Study Mode About the Role We’re looking for a hands-on Tech Lead Manager to lead and manage a team of senior full-stack engineers building AI-native learning experiences in ChatGPT. This person will combine technical execution, product judgment, and people leadership: they will write and ship code, manage engineers, and help shape the product direction for how students and Educators use AI. In This Role, You Will Lead and manage a team of three senior full-stack engineers. Build product experiences for ChatGPT Education, ChatGPT for Teachers, and AI-native learning workflows. Partner with research teams on field studies, randomized control trials, classifiers, data pipelines, and cognitive-outcome measurement. Collaborate with Consumer Learning and model teams to translate education insights into broader ChatGPT behavior and product improvements. Drive execution across product, engineering, research, go-to-market, and partner teams. Help define product strategy, priorities, and delivery plans for a new product pod. You Might Thrive In This Role If You Have several years of direct people-management experience with engineers. Are still highly technical and comfortable doing IC engineering work. Have strong pr

About the Team OpenAI’s Cyber team works to make frontier AI a decisive advantage for defenders. The Cyber Blue Team is an operator-led group focused on turning real defensive problems into better models, useful products, safe Codex workflows, and integrations with the security tools defenders already use. Our ambition is simple: Raise attacker cost. Lower defender toil. Prove it by defending OpenAI; scale it through the ecosystem. We are not setting out to build another SIEM or autonomous SOC. We want to build the AI reasoning and workflow layer that helps security teams investigate threats, create and validate detections, improve their controls, and respond with greater speed and confidence. About the Role We are looking for a Product Manager to help build a new generation of AI-powered cyber defense products. You will work closely with security practitioners, researchers, engineers, designers, internal security teams, customers, and technology partners to turn emerging model capabilities into products that solve meaningful defensive problems. This is an early-stage product role. The work will span product discovery, prototyping, evaluation, development, launch, and iteration. You will help the team identify where AI can create the most value for defenders and translate those opportunities into clear, usable, and trustworthy product experiences. Initial areas of focus may include: Detection engineering and detection-content development Threat hunting and investigation Security validation and control testing AI-agent and MCP runtime defense Integrations with security platforms and enterprise workflows Safe, governed assistance for incident response The specific roadmap will continue to evolve based on model progress, practitioner needs, internal learnings, and customer feedback. In This Role, You Will Work with security practitioners to understand high-value defensive workflows, recurring pain points, and opportunities for AI to materially improve outcomes. Help sh

About the Team The Post-Training Frontiers team is responsible for training the frontier agents OpenAI ships to the world (GPT-Next). We train the flagship agentic models behind Codex, ChatGPT, and the API through large-scale reinforcement learning. The team’s work spans four areas. First, execution and science: working with teams across OpenAI to decide what can go into the final model and how, using scientific experiments and evals that are representative of the final pipeline so issues can be recognized early. Second, RL scaling: executing the final large-scale reinforcement learning run, making sure GPUs are used efficiently and training stays healthy. Third, research: improving horizontal capabilities like instruction following, factuality, memory, and multi-agent behavior, where the team’s broad visibility helps identify cross-cutting improvements across teams and domains. Fourth, engineering: maintaining the infrastructure stack and internal tools to ensure that both the final run and all integrations go as smoothly as possible and that the systems are easy to work with. About the Role This role focuses on keeping our frontier RL training runs fast, reliable, and unblocked. You will work across engineering and infrastructure problems as they emerge, from scaling and orchestration issues to inference bottlenecks, numerical problems, and hardware failures, as well as supporting large horizontal integrations in the big run, like multi-agent capabilities or memory. This is a role for a strong generalist who quickly learns anything needed for the task, has high attention to detail, debugs deeply, and is motivated by fixing the highest-impact problem in front of the team. In this role, you will: Keep large-scale async RL training runs moving by jumping into the most urgent engineering and infrastructure problems. Debug issues across training systems, inference, orchestration, scaling, and distributed infrastructure. Improve the reliability and efficiency of RL trai

About the Team The Cybersecurity Products team builds products at the frontier of AI and cybersecurity. Our work includes Codex Security and related cyber products that turn advances in model capability into dependable tools for defenders. We help teams find, validate, and remediate vulnerabilities, continuously improve the security of software, and test AI-powered applications before they reach production. About the Role As a Full Stack Software Engineer, you will build the product experiences and systems that make AI-powered security useful in real engineering environments. You will work across web surfaces, APIs, orchestration, data models, and integrations to help security and engineering teams move from a codebase or application to evidence-backed findings, prioritized remediation, and revalidation. You will collaborate closely with product engineers, security researchers, and customer-facing teams. The work spans fast-moving product development and hard systems problems: long-running workflows, large repositories, sensitive data, reliability, observability, and a high bar for earning user trust. 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 will: Build end-to-end workflows for vulnerability discovery, security scanning, red teaming, findings review, remediation, and reruns. Design and operate backend services for long-running security work, including APIs, asynchronous orchestration, durable state, and integrations with developer workflows. Make complex security results actionable through clear product surfaces, strong evidence, thoughtful prioritization, and reliable reporting. Partner with security researchers, product teams, and users to evaluate quality, reduce noise, improve coverage, and ship safely. You might thrive in this role if you: Have experience shipping production full-stack products across modern web frontends and backend s

About the team The Agent Post-Training team creates the frontier agents OpenAI ships to the world. We are training the models behind our agents in Codex, ChatGPT, the API, and other frontier products: persistent, proactive intelligence that can operate computers, collaborate with people and other agents, and expand what people and organizations can imagine, attempt, and achieve. We define what the next generation of agents should be able to do, build the training signal that teaches those abilities, and run the experiments that make them real. Our work spans coding, tool use, computer use, multi-agent coordination, long-horizon execution, factuality, instruction following, calibrated reasoning, and taste. Our team is where new model capabilities get made. We build the data, environments, graders, training methods, and feedback loops that shape what OpenAI's next agents can do, then carry those capabilities through major training runs and into the products people use. About the Role As a researcher working on Frontier Evals & Environments, you will help build north star model environments to drive progress towards safe AGI/ASI. Your work will directly guide the research programs of the most ambitious training runs happening at OpenAI. Some prior open-sourced evaluations built by researchers in this role include GDPval , SWE-bench Verified , MLE-bench , PaperBench , and SWE-Lancer . If you are interested in feeling firsthand the fast progress of our models, and steering them towards good outcomes, this is the role for you. You will work with researchers, engineers, product teams, infrastructure teams, and safety/alignment partners to decide what should go into major model runs, measure whether it worked, and ship improvements into products used by real people. This is a high-agency role for people who want their work to land directly in frontier models. In this role, you'll: Create ambitious RL environments to push our models to their limits, and measure frontier

About the Team The Agent Post-Training team creates the frontier agents OpenAI ships to the world. We are training the models behind our agents in Codex, ChatGPT, the API, and other frontier products: persistent, proactive intelligence that can operate computers, collaborate with people and other agents, and expand what people and organizations can imagine, attempt, and achieve. We define what the next generation of agents should be able to do, build the training signal that teaches those abilities, and run the experiments that make them real. Our work spans coding, tool use, computer use, multi-agent coordination, long-horizon execution, factuality, instruction following, calibrated reasoning, and taste. Our team is where new model capabilities get made. We build the data, environments, graders, training methods, and feedback loops that shape what OpenAI's next agents can do, then carry those capabilities through major training runs and into the products people use. About the Role As a member of Agent Post-Training, Connectors, you will teach models how to interface with the top professional software using code. You will help train agents to use code, APIs, tools, and structured integrations to operate across applications like Slack, Google Workspace, GitHub, Notion, Linear, Salesforce, and other core systems of work. You will help enable models to take useful actions across a user’s digital context: finding information, updating systems, coordinating work, generating artifacts, and completing multi-step workflows through the tools teams already use. You will train models to be supercharged by the world’s most important productivity and enterprise software, turning connected tools into a powerful action surface for our agents. You will work with researchers, engineers, product teams, infrastructure teams, and safety/alignment partners to decide what should go into major model runs, measure whether it worked, and ship improvements into products used by real people.

About the Team The Agent Post-Training team creates the frontier agents OpenAI ships to the world. We are training the models behind our agents in Codex, ChatGPT, the API, and other frontier products: persistent, proactive intelligence that can operate computers, collaborate with people and other agents, and expand what people and organizations can imagine, attempt, and achieve. We define what the next generation of agents should be able to do, build the training signal that teaches those abilities, and run the experiments that make them real. Our work spans coding, tool use, computer use, multi-agent coordination, long-horizon execution, factuality, instruction following, calibrated reasoning, and taste. Our team is where new model capabilities get made. We build the data, environments, graders, training methods, and feedback loops that shape what OpenAI's next agents can do, then carry those capabilities through major training runs and into the products people use. About the Role As a member of Agent Post-Training, you will improve the capabilities, reliability, and product fit of OpenAI's agentic models. You might own a research direction, build the infrastructure that makes large training runs faster and more trustworthy, create evals that reveal where models fail, or drive a capability from an idea through experimentation, integration, and launch. This role is intentionally broad. The strongest candidates are not defined by one method or subfield; they are people who can take an ambiguous capability problem and make progress across research, engineering, data, evals, and product. You should be excited to work on models that act in the world: writing and debugging code, using tools, calling functions, operating computers, collaborating with other agents, and completing valuable work on behalf of users. You will work with researchers, engineers, product teams, infrastructure teams, and safety/alignment partners to decide what should go into major model runs, meas

About the Team The Agent Post-Training team creates the frontier agents OpenAI ships to the world. We are training the models behind our agents in Codex, ChatGPT, the API, and other frontier products: persistent, proactive intelligence that can operate computers, collaborate with people and other agents, and expand what people and organizations can imagine, attempt, and achieve. We define what the next generation of agents should be able to do, build the training signal that teaches those abilities, and run the experiments that make them real. Our work spans coding, tool use, computer use, multi-agent coordination, long-horizon execution, factuality, instruction following, calibrated reasoning, and taste. Our team is where new model capabilities get made. We build the data, environments, graders, training methods, and feedback loops that shape what OpenAI's next agents can do, then carry those capabilities through major training runs and into the products people use. About the Role As a researcher working on Frontier Evals & Environments, you will help build north star model environments to drive progress towards safe AGI/ASI. Your work will directly guide the research programs of the most ambitious training runs happening at OpenAI. Some prior open-sourced evaluations built by researchers in this role include GDPval , SWE-bench Verified , MLE-bench , PaperBench , and SWE-Lancer . If you are interested in feeling firsthand the fast progress of our models, and steering them towards good outcomes, this is the role for you. You will work with researchers, engineers, product teams, infrastructure teams, and safety/alignment partners to decide what should go into major model runs, measure whether it worked, and ship improvements into products used by real people. This is a high-agency role for people who want their work to land directly in frontier models. In this role, you might Create ambitious RL environments to push our models to their limits, and measure frontie

About the Team The Agent Post-Training team creates the frontier agents OpenAI ships to the world. We are training the models behind our agents in Codex, ChatGPT, the API, and other frontier products: persistent, proactive intelligence that can operate computers, collaborate with people and other agents, and expand what people and organizations can imagine, attempt, and achieve. We define what the next generation of agents should be able to do, build the training signal that teaches those abilities, and run the experiments that make them real. Our work spans coding, tool use, computer use, multi-agent coordination, long-horizon execution, factuality, instruction following, calibrated reasoning, and taste. Our team builds the data, environments, graders, training methods, and feedback loops that shape what OpenAI’s next agents can do and what they are like to work with, then carries those improvements through major training runs and into products used by people every day. About the Role As a member of the Agent Post-training Personality team, you will help make OpenAI’s agents exceptional collaborators. You will study what makes an agent thoughtful, clear, perceptive, appropriately proactive, and genuinely easy to work with, then translate those insights into evals, training data, reward signals, and model improvements. We use “personality” to mean much more than writing style or general likability. It includes whether an agent understands what the user is trying to accomplish, communicates with good judgment, adapts to context, asks useful questions, handles disagreement honestly and takes initiative at the right moments. The goal is to create a strong, tasteful default that can adapt to different people and situations. This work combines behavioral research, product thinking, research and communication taste. You will collaborate with product teams, human experts, and researchers across post-training and pretraining to ensure that improvements survive the full trai

About the Team The Agent Post-Training team creates the frontier agents OpenAI ships to the world. We are training the models behind our agents in Codex, ChatGPT, the API, and other frontier products: persistent, proactive intelligence that can operate computers, collaborate with people and other agents, and expand what people and organizations can imagine, attempt, and achieve. We define what the next generation of agents should be able to do, build the training signal that teaches those abilities, and run the experiments that make them real. Our work spans coding, tool use, computer use, multi-agent coordination, long-horizon execution, factuality, instruction following, calibrated reasoning, and taste. Our team is where new model capabilities get made. We build the data, environments, graders, training methods, and feedback loops that shape what OpenAI's next agents can do, then carry those capabilities through major training runs and into the products people use. About the Role As a member of this API & power-users team, you will improve the capabilities, reliability, and product fit of OpenAI’s agentic models for power users and API developers. You might design evals from real developer workflows, build training environments around production-like tool use, turn qualitative model failures into training data, evals, or post-training interventions, or drive a behavior improvement from discovery through post-training, integration, and launch. This role is intentionally broad. The strongest candidates are comfortable turning ambiguous model behavior problems into concrete progress, whether that means improving tool use, planning, instruction following, recovery from mistakes, or how models behave in API-based workflows. You should be excited to work across research, engineering, data, evals, and product to make models better at acting in real workflows. You will work closely with researchers, engineers, API/product teams, Codex, infrastructure, and safety/align

About the Team The Agent Post-Training team creates the frontier agents OpenAI ships to the world. We are training the models behind our agents in Codex, ChatGPT, the API, and other frontier products: persistent, proactive intelligence that can operate computers, collaborate with people and other agents, and expand what people and organizations can imagine, attempt, and achieve. We define what the next generation of agents should be able to do, build the training signal that teaches those abilities, and run the experiments that make them real. Our work spans coding, tool use, computer use, multi-agent coordination, long-horizon execution, factuality, instruction following, calibrated reasoning, and taste. Our team is where new model capabilities get made. We build the data, environments, graders, training methods, and feedback loops that shape what OpenAI's next agents can do, then carry those capabilities through major training runs and into the products people use. About the Role As a member of Agent Post-Training, Computer Use, you will teach models to operate computers. You will help train models that can navigate browsers and desktops, use tools and applications, reason through complex workflows, collaborate with users and other agents, and complete long-horizon tasks with reliability and judgment. This work sits at the intersection of frontier model training, product behavior, evaluation, and systems engineering, and will directly shape the computer-use capabilities shipped in OpenAI’s next generation of agents. Currently, our models are the best in the world at this behavior! You will work with researchers, engineers, product teams, infrastructure teams, and safety/alignment partners to decide what should go into major model runs, measure whether it worked, and ship improvements into products used by real people. This is a high-agency role for people who want their work to land directly in frontier models. In this role, you might Design and run experiments th

About the Team The Agent Post-Training team creates the frontier agents OpenAI ships to the world. We are training the models behind our agents in Codex, ChatGPT, the API, and other frontier products: persistent, proactive intelligence that can operate computers, collaborate with people and other agents, and expand what people and organizations can imagine, attempt, and achieve. We define what the next generation of agents should be able to do, build the training signal that teaches those abilities, and run the experiments that make them real. Our work spans coding, tool use, computer use, multi-agent coordination, long-horizon execution, factuality, instruction following, calibrated reasoning, and taste. Our team is where new model capabilities get made. We build the data, environments, graders, training methods, and feedback loops that shape what OpenAI's next agents can do, then carry those capabilities through major training runs and into the products people use. About the Role As a member of Agent Post-Training, Artifacts, you will train frontier models to create polished, useful work products: documents, spreadsheets, slide decks, dashboards, reports, analyses, and other interactive or editable artifacts. You will help teach our models to move from a vague user goal to a finished artifact with strong structure, visual taste, domain judgment, correctness, and low latency. This work will require owning improvements across our post-training stack, including RL, data pipelines, graders, reward signals, evals, and behavioral analysis. You will work with researchers, engineers, product teams, infrastructure teams, and safety/alignment partners to decide what should go into major model runs, measure whether it worked, and ship improvements into products used by real people. This is a high-agency role for people who want their work to land directly in frontier models. In this role, you will: Design and run experiments that improve agentic model behavior for complex so

About the Team The Online Data team builds and operates the core online database and indexing services for OpenAI’s production AI applications, including supporting the explosive growth of ChatGPT, the #1 AI app in the world, and Codex, the fastest growing agentic development toolset in the world. Our mission is to ensure the reliability, correctness, and scalability of our online data stack and to curate a comprehensive portfolio of services that matches the relentless ambition of OpenAI, enabling our product and research teams to build 0-100 without getting bogged down in the minutiae of multi-region, multi-cloud, exabyte-scale data infrastructure. About the Role We are seeking an Engineering Manager to lead our Online Data Systems team, responsible for our in-house database and indexing technology. This role is about shepherding a team of world-class engineers tasked with building and operating hyperscale data storage and retrieval technology. You’ll be overseeing the delivery of extremely challenging engineering work in areas like distributed query execution, multi-region federation, self-orchestrating and self-healing services, low-level performance optimization, and more. There are few companies in the world building this kind of technology in-house at this scale where you’ll still be getting in on the ground floor. Instead of being a cog in the machine spending months chasing small optimizations, you’ll play a major part of shaping our future. In this role, you will: Build, lead, and grow high-performing infrastructure engineering teams. Drive the evolution of OpenAI’s in-house online data technologies, our core, hyper-scale database systems, indexing technologies, and vector search. Anchor delivery around measurable reliability goals (SLOs, etc) to ensure system performance and resiliency is above reproach. Champion pragmatic use of agent technology to amplify execution velocity. Reduce operational toil and incident frequency through better abstractions, gua