Data Operations Lead in San Francisco

About the Team The Growth team drives user and revenue growth across ChatGPT’s consumer and business segments as well as other OpenAI products worldwide. We operate across the full funnel - from awareness and acquisition through activation, retention, and expansion - using a combination of global performance marketing, AI-powered workflows, in-product optimization, insights, experimentation, and creative ops engineering. About the Role We are hiring a Consumer Paid Marketing leader to own and scale global acquisition across channels, including Paid Search, Paid Social, Programmatic, Affiliate, and emerging channels. This leader will define channel strategy, oversee execution at significant scale, and grow a broad consumer product across diverse audiences, use cases, and international markets. You will partner closely with product, engineering, data science, finance, design, brand, creative, lifecycle, and regional marketing to connect paid experiences to in-product journeys. This team combines rapid iteration, rigorous measurement, consumer insight, and culturally relevant creative to drive acquisition, activation, retention, and long-term user value worldwide. In this role, you will: Own global paid channel strategy across Paid Search, Paid Social, Programmatic, Affiliate, and other channels for a scaled consumer product, including planning, forecasting, budget pacing, audience strategy, and performance management. Manage significant investment across a broad international portfolio, balancing global consistency with market-level opportunity, platform dynamics, and cultural nuance. Optimize the full consumer journey from acquisition through activation, engagement, retention, and monetization, with a focus on durable unit economics and cohort quality. Develop audience, use-case, offer, and creative strategies that reach diverse consumer segments and translate across languages, cultures, and stages of product maturity. Partner with regional marketing and localization

About the Team The Monetization team is a cross-functional group working across engineering, product, research, and design to build the foundational systems that will help OpenAI scale access to intelligence responsibly. Our mission is to develop user-first, privacy-preserving monetization products, including next-generation ads experiences that strengthen user trust, unlock economic opportunity, and support OpenAI’s long-term innovation. Monetization plays a critical role in enabling OpenAI to continue pushing the boundaries of AI capabilities while ensuring the benefits of AGI are broadly shared. We believe monetization must be aligned with user value, uphold rigorous privacy and safety standards, and sustain a healthy ecosystem of developers and businesses. This team operates in a greenfield environment and moves quickly through prototyping, experimentation, and iterative deployment. We partner closely with Product, Design, and Research to bring research breakthroughs into real-world systems at global scale. About the Role We’re looking for an experienced Software Engineer to build measurement systems that connect ad interactions to meaningful advertiser outcomes while protecting user privacy. In this foundational role, you’ll design infrastructure for conversion signals, attribution, reporting, and feedback loops across OpenAI’s ads products. This role is ideal for engineers who have built large-scale ads measurement, data, experimentation, marketplace, or distributed systems and want to apply that experience in a highly ambiguous 0→1 environment. You’ll work across event collection and normalization, deduplication and matching, attribution and modeled measurement, privacy-safe aggregation, reporting, and high-quality labels for ads optimization. We are hiring engineers who can independently own complex systems, make sound technical tradeoffs, and help define what should be built. You’ll work closely with Ads Delivery, Ads ML, Product, Research, Privacy, Data Sc

About the Team The Personality & Model Behavior team, within OpenAI’s broader Personal AGI team conducts research on how to shape personalities and guide the behavior of models. We think about topics such as emotional intelligence, reasoning, and how models interact thoughtfully with users. We’re particularly interested in understanding how individual users want ChatGPT to behave, and creating personalized models that feel uniquely tailored to each user. We integrate this research into ChatGPT and other OpenAI products that are used by hundreds of millions of users. About the Role We're looking for individuals with strong ML engineering skills and research experience, especially with novel and highly capable models, and in areas like reinforcement learning and reward modeling. An ideal candidate is passionate about product-driven research. In this role, you will: Conduct research around personalization, personality, and model behavior by leveraging and developing tools such as synthetic data, reward modeling, and reinforcement learning. Build robust evaluations and model training pipelines to facilitate our research. Innovate new post-training methods. 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. You might thrive in this role if you: Have a deep understanding of machine learning and its applications. Have prior knowledge in training and optimizing models and building evaluations. Are willing to dive into large ML codebases to debug issues. Thrive in dynamic and technically complex environments. Have a track record of delivering innovative, out-of-the-box solutions to address real-world constraints. 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 o

About the Team We bring OpenAI's technology to the world through products like ChatGPT and the OpenAI API. We seek to learn from deployment and distribute the benefits of AI, while ensuring that this powerful tool is used responsibly and safely. Safety is more important to us than unfettered growth. About the Role We are looking for an experienced Research Engineer to work on retrieval & search problems across our API and ChatGPT. As the AI landscape has evolved over the last few years, retrieval & search have emerged as key use cases for our models, and we are investing in ensuring that we can offer these search-based product experiences for our users. You will be at the center of our retrieval & search efforts as a company, and the progress you drive here will reach millions of end users. In this role, you will: Work on retrieval & search algorithms and methodologies in close collaboration with our research team, including problems in such domains as document search, enterprise search, knowledge retrieval, and web-scale search. Deploy these search methodologies into production in both the API and ChatGPT to be used by millions of end users. Explore novel research topics in retrieval & search that may inform our product strategy in the medium and long term. Partner with researchers, engineers, product managers, and designers to bring new features and research capabilities to the world You might thrive in this role if you: Have extensive prior experience building and maintaining production machine learning systems. Have prior experience working with vector databases, search indices, or other data stores for search and retrieval use cases Have prior experience building and iterating on internet-scale search systems Own problems end-to-end, and are willing to pick up whatever knowledge you're missing to get the job done Have the ability to move fast in an environment where things are sometimes loosely defined and may have competing priorities or de

About the Team The Intelligence and Investigations team seeks to rapidly identify and mitigate abuse and strategic risks to ensure a safe online ecosystem. We are dedicated to identifying emerging abuse trends, analyzing risks, and working with our internal and external partners to implement effective mitigation strategies to protect against misuse. Our efforts contribute to OpenAI's overarching goal of developing AI that benefits humanity. The Strategic Intelligence & Analysis (SIA) team provides safety intelligence for OpenAI’s products by monitoring, analyzing, and forecasting real-world abuse, geopolitical risks, and strategic threats. Our work informs safety mitigations, product decisions, and partnerships, ensuring OpenAI’s tools are deployed securely and responsibly across critical sectors. About the Role As a Quantitative Intelligence Analyst , you will focus on discovering novel and emerging risks in complex human–AI systems before they are well-defined, measurable, or widely understood. You will use deep subject matter expertise and quantitative tooling to surface weak, early, and unconventional risk signals. You will build analytic models that explain how harms could emerge and translate ambiguous patterns into structured, data-driven insight. Your work will help identify potential gaps in policy or coverage and operationalize previously unmeasured problems into signals that can support detection, mitigation, and planning downstream. You will develop analytical frameworks that map how new risks form, evolve, and propagate as products change, policies shift, and external events unfold. Your analyses will directly inform strategic risk prioritization and planning across the company, with regular visibility through strategic risk products. This role is based in office (hybrid, 3 days/week). Relocation support is available In this role, you will: Discover and define new quantitative risk signals where no established metrics exist, using subject matter exp

About the team The Applied team safely brings OpenAI's technology to the world. We released ChatGPT; Plugins; DALL·E; and the APIs for GPT-5, embeddings, and fine-tuning. We also operate inference infrastructure at scale. There's a lot more on the immediate horizon. Our customers build fast-growing businesses around our APIs, which power product features that were never before possible. ChatGPT is a prime example of what is currently possible. We simultaneously ensure that our powerful tools are used responsibly. Safe deployment is more important to us than unfettered growth. The Fraud Engineering team works within our Applied Engineering organization identifying and responding to fraudsters on our platform. We are looking for a software engineer with anti fraud & abuse experience to help architect and build our next-generation anti-fraud systems. About the role The Scaled Abuse team protects OpenAI’s products and customers by detecting, preventing, and responding to fraudulent and abusive behavior at scale. We build and operate the backend and data systems that power real-time detection, investigation workflows, and enforcement — balancing strong protections with a great user experience as the platform grows. Our work sits at the intersection of engineering and abuse expertise: we partner closely with Trust & Safety, Security, and Product to understand emerging attack patterns, translate messy signals into clear system behavior, and continuously harden our defenses. The problems are dynamic and ambiguous by default, so we value engineers who can quickly dive into an unfamiliar codebase, develop strong intuition about how it works end-to-end, and propose pragmatic improvements that make the entire stack more resilient. In this role, you will: Design and build systems for fraud detection and remediation while balancing fraud loss, cost of implementation, and customer experience Work closely with finance, security, product, research, and trust & safety ope

About the Team The Personal AGI team seeks to empower all of humanity to benefit from frontier intelligence in whatever way they choose. We are responsible for training models to deploy to millions of users globally via ChatGPT, the API, and future products. We aim to evolve ChatGPT from a chatbot to an infinitely capable and personalized superassistant supporting human flourishing. We work on defining, measuring, and improving capabilities across the training stack. Our focus areas include but are not limited to model behavior, personalization, safety, factuality, instruction following, personality, interactivity, multilingual fluency, world interaction, and bringing agents to everyone. We chart the course for what to strive towards. We partner closely with research and product teams across the company ensuring that our models are safe, efficient, and reliable. About the Role You’ll work as a Research Engineer / Scientist on the North Stars team within the broader Personal AGI research org. You will work on bringing the next generation of AI-enabled experiences to all of humanity by closing the capability overhang between power users and the average consumer, including areas like tool-use, feature discovery, connectors, and instruction following. You will think deeply about the current bottlenecks in model behavior, translate these insights into robust evals, training data, reward signals, and model and harness improvements. We're looking for individuals with strong ML engineering skills and research experience passionate about creative, product-driven research. 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: Own and pursue a research agenda to improve model capability and performance. Collaborate closely with the other research and product teams, allowing customers to optimize their own models. Build robust evaluations for tracking modelin

About the Team OpenAI’s API Multicloud team is responsible for extending OpenAI’s API platform into strategic cloud environments, starting with AWS . The team’s mission is to distribute OpenAI’s API broadly and safely by enabling key API technologies in cloud-native environments, in close partnership with Amazon and internal teams across Codex, Research, Safety Systems, and Applied. The team is focused on bringing core developer and enterprise capabilities into cloud-native environments, including cloud-hosted Codex, model customization / post-training as a service, and new stateful runtime environments for agentic workloads. This work sits at the intersection of production ML systems, developer platforms, model behavior, and large-scale infrastructure. About the Role We’re looking for a backend engineer who can quickly understand OpenAI’s models, products, and systems, then adapt first-party deployments for other cloud platforms. You’ll build backend services, APIs, SDK integrations, authentication flows, and cloud service infrastructure that let developers use OpenAI capabilities in the cloud environments where they already build. This role involves working across teams, sometimes embedded with partner product groups, to ship products quickly and across multiple platforms at the same time. It’s a strong fit for engineers who have built developer tools, especially AI-powered tools, communicate clearly across technical boundaries, and can shape architectures that support different deployment models; experience building cloud services is a strong plus. In this role, you will: Build backend and infrastructure systems that extend OpenAI’s API platform into cloud-native environments, like AWS. Design and ship cloud-contained products that allow customers to use OpenAI capabilities while keeping workloads and data within cloud environments. Help stand up cloud-hosted Codex experiences powered by the OpenAI Responses API. Build the infrastructure and runtime abstractions

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 ChatGPT organization at OpenAI supports our mission by bringing advanced AI capabilities to hundreds of millions of users worldwide. The Image Generation team is responsible for one of the fastest-growing experiences in ChatGPT, enabling users to create, edit, and transform images through natural language. Recent breakthroughs in multimodal AI have dramatically improved image quality, instruction following, editing precision, consistency, and text rendering. We're building the systems and experiences that turn these research advances into products used daily by creators, professionals, businesses, and consumers around the world. Our team sits at the intersection of research, product, design, and infrastructure. We work closely with model researchers, mobile engineers, frontend engineers, and platform teams to build intuitive experiences and scalable systems that power image generation at global scale. Whether users are creating marketing assets, visualizing ideas, editing photos, designing products, or simply exploring their creativity, our goal is to make visual creation feel as natural as having a conversation. About the Role We are looking for an experienced Full Stack Engineer to join the Image Generation team and help shape the future of AI-powered visual creation. In this role, you'll own features end-to-end across both frontend and backend systems, building the experiences that enable users to generate, edit, organize, and interact with images inside ChatGPT. You'll work across the entire stack—from highly interactive user interfaces and real-time workflows to backend services, APIs, orchestration systems, and data infrastructure. This role is ideal for engineers who enjoy moving fluidly between product development and systems engineering, collaborating closely with design, product, and research teams to rapidly bring new AI capabilities to users. You'll help define entirely new interaction paradigms as multimodal AI continues to evolve. In

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 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 We believe that the final enabler for AGI is spending compute on context. As a Context Researcher on Agent Post-Training, you will scale compute spent on context. You will get to work in our frontier training stack on enabling the next paradigm of model training with a clear product interface for iterative deployment (Codex Chronicle). 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 that improve scaling of compute on context. Own end-to-end improvements to the post-training stack, including RL, data pipelines, graders, reward signals, evals, diagnostics, and model-behavior analysis. Build evals and environments that expose the next set of model failures,

About the Team The Personalization-Memory team, within OpenAI's broader Personal AGI organization, is focused on developing agents that can learn from prior interactions in order to become more helpful and efficient over time. We build general-purpose memory and personalization capabilities that transfer across ChatGPT and other agentic products, and we collaborate with applied engineering on the product surfaces that allow users to interact with memory. About the Role As a Research Engineer / Research Scientist on the Personalization-Memory team, you will research and develop improvements to memory usage and personalization in OpenAI's frontier models. Our team works on reinforcement learning, dataset creation, evaluations, and other post-training methods. We partner closely with research and product teams across the company to realize the vision of a truly personalized ChatGPT. We're looking for individuals who have a background in frontier model post-training, are able to iterate quickly, and who are passionate about product-driven research. 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: Own and pursue a research agenda for improving memory use and personalization in frontier models. Build robust evaluations for tracking modeling improvements. Design, implement, test, and debug code across our research stack. Collaborate closely with the research and product teams to influence the shape of technical solutions in the product. You might thrive in this role if you: Are passionate about personalization and building personalized assistants. Have experience working with user signals and human data to turn feedback into reliable signals for training and evaluation. Have a deep understanding of frontier model post-training and machine learning applications. Value principled approaches and research craftsmanship. Are comfortable diving into a lar