Ai Platform Director in United States

About the Team The Private Computing team works across product, engineering, security, and safety to build advanced privacy products and infrastructure at OpenAI. Our mission is to provide world-class security features to users so their private data remains private, even from OpenAI. We use technologies like confidential computing, trusted execution environments, and end-to-end encryption to ship product features across ChatGPT, the API, and our future consumer devices. About the Role We’re looking for software engineers to design, build, and scale novel privacy features and infrastructure across ChatGPT, API, and future consumer devices. In this role, you will: Ship fast while balancing difficult trade-offs in complex domains Build core abstractions for trusted execution environments and end-to-end-encryption Build product features for private inference and storage across ChatGPT, API, and future consumer devices Update build systems to increase trust and verifiability Integrate with safety and integrity infrastructure Operate systems at scale with high reliability, including an on-call rotation Collaborate with a diverse set of cross-functional teams across product, engineering, security, safety, policy, and legal You might thrive in this role if you: Care deeply about user privacy and security Have 5+ years of experience in professional software engineering Have experience building and scaling confidential computing or encryption technologies in production environments Have experience with Kubernetes and cloud orchestration systems Take pride in building and operating scalable, reliable, secure systems Can collaborate well and drive alignment in the face of difficult trade-offs Are comfortable with ambiguity and rapid change Workplace & Location This role is based in San Francisco, CA. We follow a hybrid model with 4 days a week in the office and offer relocation assistance to new employees. About OpenAI OpenAI is an AI research and deployment company dedicat

About the Team At OpenAI, our User Safety & Risk Operations (USRO) team helps protect our products and users from abuse, fraud, safety risks, and other forms of misuse. We operate at the front line of real-world safety and risk management, translating user and operational signals into timely decisions, effective interventions, and improvements to our systems. This role sits on a new team within USRO focused on building operational capacity for new, ambiguous, or fast-moving areas of work. The team helps define what needs to be built, creates the operating model to support it, and works with partner teams to make the work scalable and durable over time. As a Strategic Operations Lead, you will focus on large, cross-functional initiatives that require clear thinking, technical fluency, strong execution, and the ability to bring structure to undefined problems. About the Role We are seeking a Strategic Operations Lead to drive new and existing strategic operating builds across User Safety & Risk Operations. This is a senior IC role for someone who can turn broad, undefined priorities into clear operating models, launch plans, requirements, stakeholder alignment, documentation, reporting, and execution rhythms. This role will often support initiatives where OpenAI is developing new products or partnerships and the operating model is still being defined. These programs have a direct user safety and risk nexus because new deployment models can change what signals OpenAI can see, who owns response decisions, and how user-impacting risks are detected, escalated, and resolved. You will clarify what OpenAI owns, what partner teams own, what signals we can reliably monitor, how issues should be escalated, and how the workflow should evolve from launch support into a durable operating model. The right person is highly strategic and deeply practical. They can move from executive-level framing to detailed workflow design, stakeholder management, SOPs, launch readiness, ri

About the Team OpenAI’s Financial Engineering and Identity data science team owns how revenue flows through our products and builds the systems that enable people and organizations to access OpenAI products safely, seamlessly, and at global scale. Identity sits at the critical intersection of growth, trust, and user experience. The team owns the experiences and infrastructure behind sign up, sign in, account recovery, authentication, and identity integrations across both consumer and enterprise products. As OpenAI expands across products and markets, Identity plays an increasingly important role in helping more users get started quickly while protecting them from abuse, fraud, and account compromise. About the Role We're looking for the first dedicated Data Scientist to partner with the Identity organization. In this role, you will define how we measure success across the entire identity journey—from first-time sign up and onboarding through authentication, account recovery, and enterprise identity experiences. You'll develop the experimentation frameworks, metrics, and analytical approaches that guide product decisions while helping the team navigate one of Identity's core challenges: optimizing growth while maintaining trust and security. You'll work closely with Product, Engineering, Design, Abuse, Risk, and Go-to-Market teams to identify opportunities, quantify trade-offs, and influence strategy. Some questions can be answered through A/B tests. Others require observational analyses, causal inference, and judgment under uncertainty. This is an opportunity to shape the analytical foundations of a high-impact product area from the ground up. This role is based in San Francisco, CA. We use a hybrid model (3 days/week in office) and offer relocation support. In this role, you will Define the north-star metrics and measurement frameworks used to evaluate the identity experience across consumer and enterprise products. Design and analyze experiments to optimize top-of

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.

By applying to this role, you will be considered for Research Scientist roles across all teams at OpenAI. About the Role As a Research Scientist here, you will develop innovative machine learning techniques and advance the research agenda of the team you work on, while also collaborating with peers across the organization. We are looking for people who want to discover simple, generalizable ideas that work well even at large scale, and form part of a broader research vision that unifies the entire company. We expect you to: Have a track record of coming up with new ideas or improving upon existing ideas in machine learning, demonstrated by accomplishments such as first author publications or projects Possess the ability to own and pursue a research agenda, including choosing impactful research problems and autonomously carrying out long-running projects Be excited about OpenAI’s approach to research Nice to have: Interested in and thoughtful about the impacts of AI technology Past experience in creating high-performance implementations of deep learning algorithms 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

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 Security is at the foundation of OpenAI’s mission to ensure that artificial general intelligence benefits all of humanity. The Security team protects OpenAI’s technology, people, and products. We are technical in what we build but are operational in how we do our work, and are committed to supporting all products and research at OpenAI. Our Security team tenets include: prioritizing for impact, enabling researchers, preparing for future transformative technologies, and engaging a robust security culture. About the Role As a Security Engineer, Application Security you will be responsible for identifying and mitigating security vulnerabilities within software applications through building security tools, code reviews, penetration testing, and security assessments. We’re looking for people who will work closely with development teams to ensure secure coding practices are integrated throughout the software development lifecycle, preventing security risks before they emerge. You will also provide security guidance to developers and other stakeholders, fostering a culture of security awareness within the organization. The role is preferred to be based in San Francisco, Seattle or New York City but may consider remote work. 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: Perform Security Assessments : Conduct regular security assessments, code reviews, and penetration testing to identify vulnerabilities in applications and software. Develop and Implement Security Tools : Design, develop, and implement security tools, frameworks, and methodologies to protect applications against security threats. Collaborate with Development Teams : Work closely with development teams to ensure security best practices are integrated throughout the software development lifecycle (SDLC), including secure coding guidelines. Threat Modeling and Risk Assessment : Conduct threat modeling and risk

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 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 Our Sales team has a unique mission to help customers understand the deep impact that highly capable AI models can bring to their business and clients. As an Account Director focused on Professional Services, you will partner with leading global consulting firms, systems integrators, IT services providers, and business process outsourcing organizations to help them transform how they deliver services, increase workforce productivity, accelerate software development, and build AI-powered offerings for their clients. This role is a mixture of technical understanding, executive engagement, strategic partnership, and value-driven selling. You'll work closely with customer executives to identify high-impact AI opportunities across consulting, delivery, engineering, operations, and managed services while collaborating internally with researchers, engineers, and solution strategists to bring these capabilities to market. You’ll be a key driver of opportunities through the entire sales cycle, from pipeline generation to closure, helping some of the world's largest professional services organizations reimagine how work is delivered with AI. This role is based in San Francisco or New York City. We use a hybrid work model of 3 days in the office per week and offer relocation assistance to new employees. We are open to US-based remote candidat

About the Team OpenAI’s Strategic Finance organization provides financial insights and guidance to support the company’s long-term strategy and ambitious growth. We partner across Product, Partnerships, Engineering, and GTM to allocate resources to the highest-impact opportunities while protecting sustainable unit economics. The Applications Technology Strategic Finance team partners closely with Product to scale core offerings and bring new products to market. We lead revenue forecasting and performance management across the product portfolio, including economic margins, pricing, and partnership deal pricing and structuring. About the Role We are hiring on our Strategic Finance Partnerships team to drive strategic decision-making across OpenAI’s growth and media partnership portfolio, focused on our consumer business. This role will be a critical finance partner to Partnerships deal leads and Product owners, responsible for evaluating, pricing, and structuring partnerships, and translating partner opportunity into decision-ready economics and performance management. The role will be hands-on in deal evaluation, negotiation support, and portfolio oversight. You will shape how OpenAI approaches partnership unit economics, measurement and incrementality, and how partnership performance is incorporated into forecasts and planning. This role is based in San Francisco, CA, with a hybrid work model (3 days/week in-office). Relocation assistance is available. In this role, you will: Own financial evaluation for growth and media partnerships, including modeling revenue, margin, compute, and payback to support decision-making and negotiations. Develop clear economic frameworks and deal guardrails (pricing floors, rev-share structures, caps, minimum guarantees, performance-based terms) to protect long-term unit economics. Partner closely with Product and Partnerships to define and measure incrementality and ensure contract terms align with measurable outcomes. Lead partnershi

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

About the Team OpenAI’s Safety teams work to ensure our products are safe, trusted, and resilient as frontier AI systems scale globally. We tackle some of the company’s most important challenges across understanding and preventing misuse and misalignment, intercepting fraud and abuse, and protecting vulnerable users. We are hiring Data Scientists to help build the analytical foundations that allow OpenAI to deploy increasingly capable AI responsibly. We are hiring Data Scientists across several teams that contribute to safety in different ways, including: Safety Systems Integrity Product Policy This is a high-impact role operating at the intersection of product, safety, policy, and research. About the Role As a Data Scientist, Safety, you will help solve complex and ambiguous problems where rigorous analysis directly informs critical decisions. Depending on your background and team alignment, you may work on areas such as: Measure harmful or abusive behavior across OpenAI’s products Detect fraud, manipulation, coordinated misuse Evaluate and improve safety classifiers, rules systems, mitigation systems, and human review workflows Design experiments and causal analyses to understand product, policy, and mitigation impacts Build prevalence estimators, dashboards, monitoring systems, and executive decision frameworks Diagnose gaps in safety and integrity systems using behavioral and product data, and help quantify and navigate false positive / false negative tradeoffs Translate ambiguous safety risks into measurable problems and evidence-based recommendations Partner with Product, Engineering, Policy, Research, and Operations teams to improve safety outcomes Build zero-to-one analytical systems in rapidly evolving domains Ideal Candidate We’re looking for strong Data Scientists who thrive in ambiguous, high-leverage environments. You may be a fit if you have: Strong statistical reasoning and analytical judgment Experience with experimentation, causal inference, or obse