Product Support in United States

About the Team OpenAI’s mission is to ensure that artificial general intelligence benefits all of humanity. Customer Education plays an important role in that mission by helping people and organizations use increasingly capable AI systems effectively, responsibly, and with confidence. We help customers build the confidence and practical skills to use OpenAI effectively, change how work gets done, and realize lasting value. About the Role Helping organizations adopt AI takes more than access to powerful technology. People across the workforce need the skills and confidence to use it well. Within Customer Education, you will lead the enterprise learning program and team. You will set direction, develop the team, and build a clear, high-quality learning portfolio that moves customers from first learning to confident use and meaningful business impact. This is a rare opportunity to define how customers learn with OpenAI at global scale, alongside the teams shaping the technology. In this role, you will: Lead and develop a high-performing Customer Learning team. Own the customer-facing Learn experience, curriculum, and program portfolio—from strategy through results. Turn priority customer needs into role-based tracks, paths, and high-quality programs. Make the portfolio easy to understand, with clear starting points, progression, and relationships among experiences. Lead how learning reaches customers through owned and partner channels. Use customer and product evidence to measure impact and decide what to build, scale, improve, defer, or stop. You might thrive in this role if you: Build strong teams, grow talent, and create accountability. Shape education strategy from customer needs and turn it into programs with measurable impact. Make clear portfolio choices about investment, capacity, and focus. Turn complex learning into clear journeys customers can understand and use. Reach customers through owned and partner channels, building trust across teams. Qualifications

About the Team OpenAI’s Business Marketing team helps organizations understand, adopt, and create value with AI. Integrated Marketing connects our brand, products, customers, developer communities, go-to-market teams, and commercial priorities into coherent market moments. We are building an AI-native marketing team that moves quickly, uses intelligence to work differently, and measures success by whether business audiences become aware of OpenAI, consider our products, and ultimately choose us. About the Role We’re hiring an integrated marketer to shape how businesses understand, experience, and choose OpenAI. You’ll connect our brand, products, and customer stories into campaigns that demonstrate how AI is transforming organizations and establish OpenAI as a preferred partner for businesses adopting AI. You’ll build integrated campaigns for BDMs, and grow customer relationships through storytelling as a critical source of proof of enterprise transformation.. This role is part of the Integrated Marketing team within OpenAI’s Business Marketing organization. While your specialty is brand and customer, you’ll flex across business campaigns, product launches, and other priority market moments. In this role, you will: Lead integrated brand marketing strategies and campaigns that increase awareness, consideration, and preference among enterprise buyers and business decision makers. Synthesize audience research, cultural/competitive context, product truth, customer proof, and business priorities into positioning and creative briefs that can travel across paid, owned, earned, social, field, and sales channels. Connect brand, creative, customer marketing, communications, media, growth, demand generation, and field activation into one coherent business audience journey. Use AI to uncover insights, experiment with new storytelling formats, and accelerate high-quality execution. Be directly accountable for increasing unaided awareness, consideration, purchase preference, and

About the Role OpenAI’s brands are trusted by hundreds of millions of users around the world. As awareness and adoption of OpenAI’s products continue to grow, so does the volume and sophistication of brand abuse, including impersonation, scams, copycat applications, fraudulent websites, social media misuse, and other forms of online infringement. We are seeking an experienced Brand Protection Manager to build and operate OpenAI’s global brand protection program. This role will lead efforts to identify, prioritize, and address misuse of OpenAI’s brands across websites, social media platforms, app stores, marketplaces, advertising networks, and other online ecosystems. The ideal candidate combines strong operational execution, investigative instincts, and program management skills. They are comfortable working across Legal, Marketing, Comms, Security, Trust & Safety, and external partners to address emerging threats and develop scalable enforcement programs. This role will lead OpenAI’s Brand Protection & Operations function and help ensure that OpenAI’s brands remain trusted, protected, and resilient as the company continues to grow globally. 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 and operate OpenAI’s global brand protection program. Monitor and respond to misuse of OpenAI brands across websites, social media, marketplaces, app stores, advertising platforms, and emerging online ecosystems. Develop and manage programs to protect OpenAI products, services, and associated brands. Coordinate investigations and enforcement efforts across online and offline channels. Partner with product, marketing, security, trust & safety, and legal teams to address brand abuse and emerging threats. Develop enforcement playbooks, prioritization frameworks, and escalation processes. Manage relationships with brand protection vendors, mon

About the Role As Enterprise Sales Manager, Banking, you will build and lead a team of Account Directors focused on driving strategic growth across the world’s largest banking institutions. You’ll be a hands-on front-line leader responsible for helping your team navigate complex enterprise sales cycles, deepen executive relationships, and drive adoption of OpenAI’s platform across highly regulated banking environments. This role will shape how OpenAI partners with global banks as they rethink productivity, operations, customer engagement, risk management, and software development through AI. You will help define the GTM strategy, operating model, and industry playbook for one of OpenAI’s most strategic verticals. Key Responsibilities Recruit, develop, and lead a high-performing team of Account Directors focused on large enterprise banking institutions across North America. Create a strong coaching culture with frequent deal reviews, account strategy sessions, ride-alongs, and structured 1:1s that elevate team performance. Define and refine the GTM strategy for banking, including account prioritization, executive engagement strategy, and territory planning. Drive disciplined pipeline generation, forecasting accuracy, and operational rigor across a complex enterprise sales motion. Guide teams through highly consultative, multi-stakeholder opportunities involving technology, security, compliance, legal, and procurement stakeholders. Help customers translate AI and API capabilities into measurable business outcomes across banking use cases such as employee productivity, customer operations, risk and compliance, software engineering, knowledge management, and workflow automation. Lead teams selling into highly regulated environments with a deep understanding of banking-specific risk, governance, and data security considerations. Partner closely with Product, Solutions Architecture, Technical Success, Legal, Security, and Finance to help customers successfully deploy AI a

About the Team The Infrastructure Engineering function sits within IT and is responsible for reliably building, deploying, and operating critical on prem and hybrid environments that power internal services and critical R&D environments. This is an early, high-leverage technical role focused on applying strong Site Reliability Engineering discipline to environments where uptime, safety, recoverability, and security are non-negotiable. This person helps replace bespoke, one-off infrastructure with standardized infrastructure-as-code building blocks that compound reliability and operational leverage as OpenAI scales. About the Role We are looking for an experienced Site Reliability Engineer working on security infrastructure to design, build, and operate reliable, secure, and scalable infrastructure that underpins identity, access, endpoint, and shared platform services across the company. In this role, you will be a senior technical owner for infrastructure and identity systems end to end, from architecture and implementation through policy enforcement, upgrades, recovery, and day-two operations. You will build durable, production-grade platforms that remove operational friction, enforce security by default, and enable teams to move faster with confidence. This role is well suited for a hands-on senior engineer who thrives in ambiguity, enjoys owning complex systems end to end, and raises the reliability and security bar by replacing fragile implementations with standardized, repeatable infrastructure. This role is based in our San Francisco HQ and requires in-office presence. In this role, you will: Design, build, and operate reliable infrastructure across on-prem, hybrid, shared, and product adjacent environments. Establish standardized infrastructure patterns that replace bespoke implementations with repeatable, auditable, secure-by-default systems. Own the lifecycle of critical infrastructure platforms, including provisioning, deployment, upgrades, patching,

About the team ​​Preparedness is a critical Safety Research team at OpenAI, which is focused on mitigating AI threats to global security that could scale to an extreme level of severity. Our work involves: Measurement. Monitoring and predicting the evolving capabilities of frontier AI systems. Mitigation. Keeping misuse safeguards, alignment tools, and security measures on track to adequately address extreme threats that might arise in the future. Coordination. Setting mitigation targets by maintaining OpenAI’s preparedness framework , and partnering with other staff to achieve these targets. This is urgent, fast-paced work that has far-reaching implications for the company and for society. About the role Models are becoming increasingly capable—moving from tools that assist humans to agents that can plan, execute, and adapt in the real world. As we push toward AGI, cybersecurity becomes one of the most important and urgent frontiers: the same systems that can accelerate productivity can also accelerate exploitation. As a Researcher for cybersecurity risks, you will help design and implement an end-to-end mitigation stack to reduce severe cyber misuse across OpenAI’s products. This role requires strong technical depth and close cross-functional collaboration to ensure safeguards are enforceable, scalable, and effective. You’ll contribute directly to building protections that remain robust as products, model capabilities, and attacker behaviors evolve. In this role, you will: Design and implement mitigation components for model-enabled cybersecurity misuse—spanning prevention, monitoring, detection, and enforcement—under the guidance of senior technical and risk leadership. Integrate safeguards across product surfaces in partnership with product and engineering teams, helping ensure protections are consistent, low-latency, and scale with usage and new model capabilities. Evaluate technical trade-offs within the cybersecurity risk domain (coverage, latency, model util

About the Team OpenAI's Legal team plays a crucial role in furthering OpenAI's mission by tackling innovative, fundamental legal issues in AI. If you're passionate about doing significant and unique work as a technology lawyer, this team is for you. The team comprises legal professionals from diverse fields, including technology, privacy, IP, corporate, cybersecurity, employment, tax, regulatory, and litigation. About the Role We’re growing our world-class Legal team and seek an experienced counsel to lead our global hardware IP portfolio initiatives, including patent, trademark and other intellectual property matters related to our business. This role is highly cross-functional across OpenAI, including work across our Legal, Communications, Global Affairs, Product, Research and Executive teams. 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 global hardware IP initiatives, including setting and executing on the strategic direction and management of our hardware IP portfolio. Create scalable processes internally and externally with outside counsel to build, maintain and protect our hardware IP portfolio. Developing and maintaining internal hardware IP policies and programs. Engaging externally on hardware IP policy issues. Advising on strategic hardware IP deals. Advising on hardware IP issues, ranging from patent, trademark, trade secret to open source. Developing and building internal AI expertise, processes and tools to facilitate legal team work. Experience advising on complex technology transactions and inbound technology licensing. You might thrive in this role if you: Have at least 10+ years of combined hardware IP experience at innovative technology companies and law firms. Have a JD and license or qualification to practice in CA. Have a strong sense of ownership, are inquisitive and enthusiastic about technology, enjoy being con

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 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 Agent Infrastructure team at OpenAI is responsible for building systems that enable training and deployment of highly useful AI agents, both internally and for the world. We work hand-in-hand with researchers to design and scale the environment in which agentic models are trained – providing a workspace for AI models to execute code, debug issues, and develop software just as human SWEs do. Our training environment for agentic models operates at an extremely high scale and has the flexibility to emulate any environment in which an agent might work. At the same time, our team builds and maintains OpenAI’s core platform for the deployment and execution of agents in production. Our systems power products such as Codex, Operator, tool use in ChatGPT, and future agentic products. Some of the most challenging technical problems in scaling the capabilities and utility of agents and agentic models lie in the infrastructure layer – and our team is focused on building the research and production systems that enable OpenAI to train the most capable models in the world, and maximize the utility of our agentic products for users around the world. About the Role As a Software Engineer on the Agent Infrastructure team, you will have the opportunity to work closely with both research and product at OpenAI - building and scaling systems to train highly capable agentic models, and building the platform and integrations to launch new agents to hundreds of millions of users worldwide. Your work will consist of both building new capabilities - standing up the infrastructure and integrations needed to train more complex agentic models - and rapidly scaling these new capabilities to some of the largest compute clusters in the world. At the same time, you’ll be instrumental to the launch of agentic products at OpenAI - building, maintaining, and scaling the production platform on which all agents run. We’re looking for people with deep experience building AI infrastructu

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