Data Governance Analyst Banking in United States

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 OpenAI’s People Experience & Technology (PXT) team owns the core people platform that powers worker, recruiting, contingent, approvals, and lifecycle workflows across the company. PXT is responsible for operating Workday, Ashby, and related people systems as governed, reliable sources of truth, while building the controls, monitoring, documentation, and auditability required to support scale. About the Role We’re hiring an Enterprise Systems Manager, Recruiting Systems to help own and harden OpenAI’s recruiting platform, with a focus on Ashby and its connected workflows. This is a hands-on systems role for someone who can translate recruiting process problems into governed, durable fixes through configuration, workflow design, access controls, documentation, reporting guardrails, and integration partnership. You will work at the boundary of Recruiting, HR Operations, Legal, Compensation, Analytics, IT, and PXT to improve the reliability and control health of recruiting workflows. The right person is comfortable going deep in system design while also driving rollout, adoption, and operational clarity. In this role you will: Own specific recruiting workflow domains in Ashby and adjacent tools, including stages, fields, permissions, approvals, templates, and configuration standards. Partner on high-priority remediation work across start dates, offers, approvals, integrations, auditability, data integrity, and workflow controls. Design and implement governed workflow changes that balance recruiter usability with reporting trust, downstream integration reliability, and control requirements. Establish and maintain guardrails such as required and conditional fields, stage definitions, role-based permissions, approval logic, validation patterns, and change standards. Drive durable fixes for recurring operational issues by identifying root causes and resolving them through configuration, automation, documentation, or process redesign. Partner with PXT, IT,

About the team OpenAI’s mission is to build safe artificial general intelligence (AGI) which benefits all of humanity. This long-term undertaking brings the world’s best scientists, engineers, and business professionals into one lab together to accomplish this. In pursuit of this mission, our Go To Market (GTM) team is responsible for helping customers learn how to leverage and deploy our highly capable AI products across their business. The team is made of Sales, Solutions, Support, Marketing, and Partnership professionals that work together to create valuable solutions that will help bring AI to as many users as possible. About the role The Go To Market (GTM) team at OpenAI is dedicated to helping customers realize the transformative potential of AI technology. We are seeking a Revenue Operations Manager to support and optimize the operations within our Technical Success team, working closely with cross-functional teams to drive operational excellence and maximize customer impact. As a Revenue Operations Manager, you will focus on designing processes that enhance the customer experience, optimizing internal workflows, and delivering data-driven insights that support strategic decision-making across teams. In this role, you'll: Design and implement processes to support the OpenAI Technical Success team, ensuring seamless management of customer interactions throughout the lifecycle, from onboarding to ongoing technical support and services delivery. Work closely with technical teams to ensure that operational processes are scalable and efficiently integrated into customer success workflows. Define and track key performance indicators (KPIs) specific to technical success, developing and maintaining dashboards to provide actionable insights to stakeholders and leadership. Manage capacity and coverage strategy for the Technical Success team, working with leadership to ensure alignment with business objectives and offering data-driven recommendations for improvements. P

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 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 OpenAI’s mission is to build safe artificial general intelligence (AGI) which benefits all of humanity. This long-term undertaking brings the world’s best scientists, engineers, and business professionals into one lab together to accomplish this. In pursuit of this mission, our Enterprise Platform team builds the systems that help our GTM teams bring OpenAI products to customers at scale. This role sits close to our emerging ads business, partnering with Ads Sales, Operations, Product, Finance, Legal, and Engineering to create reliable workflows for advertiser lifecycle, campaign readiness, approvals, and revenue operations. About the role Our GTM team helps customers understand the transformational potential of OpenAI’s models, and our internal systems should make that work faster, cleaner, and more intelligent. We’re looking for a Salesforce Ads Systems Engineer to design and build the Salesforce foundation for our ads business. You’ll partner with Ads Sales, Ads Operations, Product, Finance, Legal, and data teams to turn complex advertiser and campaign workflows into scalable, auditable systems. This is an execution-heavy, builder role. The primary charter of this role is ads GTM systems: advertiser account and opportunity workflows, campaign/order readiness, approvals, integrations, and automation that keep ads motions moving cleanly from pipeline through launch, billing, measurement, and reporting. In this role, you'll: Build Salesforce solutions for ads GTM : Develop user experiences, objects, automations, and guardrails that help Ads Sales and Operations manage advertisers, opportunities, insertion orders, campaign readiness, approvals, and launch handoffs with high data quality. Engineer integrations across the ads stack : Connect Salesforce with ads platforms, product catalogs, pricing/rate-card systems, data warehouses, billing tools, measurement workflows, CLM, and e-signature systems so advertiser and campaign data stays accurate and audit

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,