Product Partnerships Manager in United States

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

This role will support the fleet infrastructure team at OpenAI. The fleet team focuses on running the world’s largest, most reliable, and frictionless GPU fleet to support OpenAI’s general purpose model training and deployment. Work on this team ranges from Maximizing GPUs doing useful work by building user-friendly scheduling and quota systems Running a reliable and low maintenance platform by building push-button automation for kubernetes cluster provisioning and upgrades Supporting research workflows with service frameworks and deployment systems Ensuring fast model startup times though high performance snapshot delivery across blob storage down to hardware caching Much more! About the Role As an engineer within Fleet infrastructure, you will design, write, deploy, and operate infrastructure systems for model deployment and training on one of the world’s largest GPU fleet. The scale is immense, the timelines are tight, and the organization is moving fast; this is an opportunity to shape a critical system in support of OpenAI's mission to advance AI capabilities responsibly. 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: Design, implement and operate components of our compute fleet including job scheduling, cluster management, snapshot delivery, and CI/CD systems. Interface with researchers and product teams to understand workload requirements Collaborate with hardware, infrastructure, and business teams to provide a high utilization and high reliability service You might thrive in this role if you: Have experience with hyperscale compute systems Possess strong programming skills Have experience working in public clouds (especially Azure) Have experience working in Kubernetes Execution focused mentality paired with a rigorous focus on user requirements As a bonus, have an understanding of AI/ML workloads About OpenAI OpenAI is an AI resea

About the Team The Strategic Finance team provides financial insights and guidance to support the organization's long-term goals and strategies. We partner across the business to allocate and deploy our resources for the highest impact outcomes. About the Role We are hiring a Hardware Finance Manager, Consolidations & Systems to own the consolidated financial reporting, forecasting cadence, and financial systems across our hardware organizations (Io and Robotics). This role sits at the intersection of Hardware Finance, central FP&A, Accounting, and Finance Systems, and is responsible for ensuring leadership has a single, accurate, and timely financial view of the hardware business. This role will serve as the embedded owner of the hardware financial backbone, with end-to-end responsibility for consolidated P&L reporting, forecasting and variance analysis, and the systems and processes that enable scalable, decision-ready insights. You will help ensure hardware financials are consistent, auditable, and fit for executive and board-level decision-making as the organization scales in size, complexity, and capital intensity. We have a strong preference for candidates who can be based in our San Francisco HQ. 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 oversee multiple areas of responsibility: Own end-to-end consolidated financial reporting for all hardware organizations (Io and Robotics), delivering a single, integrated P&L view across R&D, Product, Revenue, COGS, and OpEx. Establish and run the hardware financial operating cadence, including monthly close, forecasting, reforecasting, and variance analysis, ensuring consistency and accountability across teams. Serve as a key finance partner to Hardware Finance leads, central FP&A, and Accounting, aligning bottoms-up plans with accounting actuals and enterprise reporting requirements. Build and maintain consoli

About the Team The Future of Computing Research team is an Applied Research team within the Consumer Devices group focused on developing new methods and models as we advance forward in our mission of building AGI that benefits all of humanity. As a Software Engineer on the Future of Computing Research team, you will work together with both the best ML researchers in the world and the greatest design talent of our generation to push the frontier of model capabilities. About the Role We are looking for a Software Engineer to join our team to build tools and services that enable AI research, evaluation, and data generation workflows. The best work in this role will start with an ambiguous design question and turn it into working research systems. You will work closely with researchers, designers, and engineers to build the evaluation systems, synthetic data generation pipelines, review tools, and supporting platform services. The goal is to make these workflows easier to create, run, and trust without requiring bespoke engineering support for each new design concept. You will help ensure that research artifacts have a clear lifecycle, runs are reproducible and observable, and results provide useful evidence for product and model-training decisions while the underlying systems remain reliable and reusable. This role is based in San Francisco, CA. We use a hybrid work model of three days in the office per week and offer relocation assistance to new employees. In this role, you will: Build web applications, APIs, data models, and backend services for AI research workflows. Build tools to author and manage evaluation tasks, rubrics, graders, suites, and rollout configurations, including workflows for publishing, versioning, auditing, and sharing research artifacts. Automate evaluation runs and generate useful reports for design, research, and engineering teams. Support synthetic data generation workflows for multimodal and conversational research, including tools that comb

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

About the Team Our Safety Systems team is at the forefront of OpenAI's mission to build and deploy safe AGI, driving our commitment to AI safety and fostering a culture of trust and transparency. Within Safety Systems, the Model Policy team aligns model behavior with desired human values and norms. We co-design policy with models and for models by driving rapid policy taxonomy iteration based on data and defining evaluation criteria for foundational models’ ability to reason about safety. About the Role Frontier AI systems are rapidly expanding what is possible in cybersecurity and software engineering. These capabilities create major defensive opportunities, but they also raise serious dual-use and misuse risks across areas such as malware development, exploit discovery, vulnerability chaining, credential abuse, cyber intrusion, and autonomous offensive operations. In this role, you will help define how OpenAI’s models should behave in high-risk cybersecurity contexts. You will develop policy frameworks, threat models, taxonomies, evaluations, and behavioral specifications that guide model behavior across training, deployment, and monitoring systems. This role sits at the intersection of cybersecurity, AI safety, threat modeling, evaluation science, and policy implementation. You will work closely with research, engineering, safety training, preparedness, and product teams to build policies that are technically grounded, measurable, enforceable, and responsive to real-world cyber risk. Your Responsibilities: Design and maintain model policies for cybersecurity and frontier-risk domains, especially dual-use and high-risk cyber capabilities. Translate cybersecurity threat models into clear behavioral specifications, evaluation criteria, grading guidance, and system-level mitigations. Define practical boundaries between legitimate security research, defensive workflows, and assistance that could materially enable harmful activity. Build policy artifacts that support i

About the Team The Hardware Health and Observability team owns the end-to-end health lifecycle of OpenAI’s global compute fleet. Our mission is to maximize healthy, usable compute across accelerator vendors, generations, cloud providers, and regions through reliable health signals, automated remediation, and scalable operational tooling. We build the systems that observe, detect, remediate, and verify hardware issues across GPUs, CPUs, networking, and platform infrastructure, enabling frontier model training and inference workloads to run reliably at hyperscale. We are the last line of defense for the success of OAI’s production and research workloads. About the Role On the Hardware Health and Observability team, you’ll build critical infrastructure that keeps OpenAI’s largest compute clusters healthy and operational at scale. Even small numbers of unhealthy systems can impact large-scale training and inference workloads. This team focuses on minimizing downtime, improving fleet efficiency, and ensuring compute resources remain continuously available to researchers and product teams. Engineers on this team own problems end-to-end, from defining health signals and debugging failures to building automated remediation systems that operate across millions of GPUs globally. In this role, you will: Define and maintain health signals across GPUs, CPUs, networking, and platform infrastructure. Build and evolve health checks that detect, remediate, and verify failures at scale. Ensure critical health checks execute with minimal latency to maximize workload uptime. Investigate hardware failures and system-level issues across large-scale compute environments. Own node lifecycle workflows including drain, quarantine, repair, RMA, and return-to-service processes. Build automation and tooling that enables global cluster management with minimal manual intervention. Partner with workload, reliability, and provider teams to integrate health signals into training and inference system

About the Team At OpenAI, we’re building the connective tissue between our mission and our people. People Innovation Labs is a fast-moving engineering team embedded in the People organization, focused on rethinking how we find and retain the best talent and empower everyone to do their best work. From recruiting to culture, we’re designing systems that give our People Team a significant edge by infusing OpenAI’s models and first-principles thinking into every aspect of our work. Our projects range from greenfield 0-1 products like OpenHouse (our internal knowledge hub) to AI-powered automations and scalable recruiting tools. We’re defining the future of work at OpenAI, creating a blueprint for how AI can supercharge productivity, culture, and innovation. About the Role We’re seeking a Data Engineer to build data-intensive systems that will power People Innovation Labs’ internal products and enable the People Analytics function to do their best work. These data pipelines are crucial for our build-out of people products backed by business systems of record and for ongoing people data analytics. One example of an employee-facing product you’ll help us build is OpenHouse, which serves as a culture and communication hub and an organization-wide front door into all other aspects of People Innovation Labs’ work. OpenHouse and other products in our portfolio are built by full stack product engineers who are deeply curious about culture, recruiting and people development, and want to know everything from the business strategy and metrics down through the code that gets us there. In this role, you will work with People Innovation Labs leadership and software engineers and the People Analytics team to build the data systems that enable this work. In this role, you will: Design, build and manage people data pipelines, ensuring all data is seamlessly integrated into our Databricks warehouse. Develop canonical datasets to track key people metrics and People Innovation Labs produc

AI Systems Engineer - Codex Core Agents About The Team The Codex Core Agents team builds the agent harness that turns model capability into real-world action. We own the systems around the model: prompting and interpreting model outputs, executing actions safely in real environments, and feeding production experience back into better models and better agent behavior. This team sits close to research and works across the stack: harness, model interaction, inference, sandboxed execution, orchestration, evals, production reliability, and the performance envelope around tokens, latency, cost, capacity, and quality. The harness is open source and increasingly part of how models are trained and evaluated, making this one of the highest-leverage layers in Codex. About The Role We’re looking for engineers to build the AI systems that make Codex agents dependable in production. The ideal candidate is an agent-systems builder: hands-on across low-level systems and ML workflows, able to debug Codex behavior end to end across the harness, model behavior, inference/runtime stack, GPU fleet, and product surface. You’ll work with research, infrastructure, and product to design agent harness capabilities, run experiments and ablations across the model + system prompt + harness stack, build frameworks for assessing production agent performance, and turn messy failures into durable improvements. What You’ll Do Design and build the core agent harness and execution loop that lets Codex agents interpret model outputs, use tools, execute code, and complete long-horizon tasks safely. Build sandboxing, isolation, orchestration, state, and workflow infrastructure for agents operating in real development environments. Develop evaluation, experimentation, and debugging systems that distinguish harness issues, model behavior, inference/runtime issues, and product failures. Run ablations across prompts, model-facing interfaces, context construction, tool-use strategies, and harness behavior to

About the Team The Integrity team at OpenAI is dedicated to ensuring that our cutting-edge technology is not only revolutionary, but also secure from a myriad of adversarial threats. We strive to maintain the integrity of our platforms as they scale. The Integrity team is at the front lines of defending against misuse in all its forms: content abuse, scaled attacks, and other actions that could undermine the user experience or harm our operational stability. About the Role As a Machine Learning Engineer in OpenAI's Integrity team, you will have the opportunity to work with some of the brightest minds in AI. You’ll work on state-of-the-art models and classifiers, experiment with new architecture and approaches, and push forward our abilities in content and user understanding. You’ll help turn research breakthroughs into tangible solutions that improve the trust and safety of our platform. If you're excited about training LLMs and building ML models, this role is your chance to make a significant mark. In this role, you will: Innovate and Deploy: Design and deploy advanced machine learning models that solve real-world problems. Bring OpenAI's research from concept to implementation, creating AI-driven applications with a direct impact. Collaborate with the Best: Work closely with researchers, software engineers, and product managers to understand complex business challenges and deliver AI-powered solutions. Be part of a dynamic team where ideas flow freely and creativity thrives. Optimize and Scale: Implement scalable data pipelines, optimize models for performance and accuracy, and ensure they are production-ready. Contribute to projects that require cutting-edge technology and innovative approaches. Learn and Lead: Stay ahead of the curve by engaging with the latest developments in machine learning and AI. Take part in code reviews, share knowledge, and lead by example to maintain high-quality engineering practices. Make a Difference: Monitor and maintain deployed m

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

About the Team 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 We’re seeking an exceptional Principal-level Offensive Security Engineer focused on deep, hands-on penetration testing of OpenAI’s agent-powered products, infrastructure, and model-integrated application surfaces. You’ll assess complex systems end to end, identify realistic vulnerabilities, validate exploitability and impact, and partner closely with engineering teams to drive durable fixes. This role will be primarily focused on continuously testing our agent-powered products like Codex and Operator. These systems are uniquely valuable targets because they’re rapidly evolving, can perform sensitive actions on behalf of users, and have large, diverse attack surfaces. You will play a crucial role in securing our agents by finding vulnerabilities that emerge from the interactions between the applications, infrastructure, tools, and models that power them. You’ll have the chance to not only find vulnerabilities, but actively drive their resolution, build reusable testing approaches, automate offensive security workflows with cutting-edge technologies, and use your attacker perspective to improve the security of OpenAI’s products. In this role you will: Conduct deep penetration tests of OpenAI’s agent-powered products, including web applications, APIs, cloud services, identity and authorization flows, CI/CD systems, and model-integrated product surfaces. Continuously hunt for exploitable vulnerabilities in the interactions between the appli

About the Team Our infrastructure team helps deliver OpenAI’s most capable models and products to the world by scaling infrastructure and turning demand into useful FLOPS. We collaborate across research, engineering, design, and business to turn cutting-edge AI advancements into impactful, real-world applications. Our team ensures the right compute is available—at the right time and place—to support some of the world’s most demanding workloads. We empower all of OpenAI’s products and research by scaling the infrastructure behind them. Our work makes it possible to launch new models and products reliably and at scale. About the Role As a Data Scientist on the Infra team, you will play a key role in shaping how we scale the infrastructure that powers OpenAI’s products and research. This is critical as we operate one of the largest and most advanced compute fleets in the world, supporting millions of users and businesses globally. We focus on aligning infrastructure measurement, planning, scaling, allocation, and efficiency to drive measurable impact across the company. You should expect to guide the definition of foundational datasets for infrastructure resources, develop metrics that inform key decisions, build forecasting and optimization models, and establish source of truth dashboards and analyses that enable teams to understand and improve infra usage. Most importantly, you should expect to be a core partner to engineering, research, and product teams in shaping the infrastructure that powers everything OpenAI builds. 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 maintain foundational datasets and metrics that reflect infrastructure usage, efficiency, and scaling. Develop forecasting and optimization models to support infra planning and resource allocation. Partner with engineering, research, and product teams to shape infrast

About the team The Applied AI Engineering team is responsible for ensuring the safe and effective deployment of Generative AI applications for developers and startups. We act as a trusted advisor and thought partner for our customers, working to build an effective backlog of GenAI use cases for their industry and drive them to production through strong technical guidance. OpenAI's customers represent a range of diverse backgrounds and maturity, from early-stage startups to late-stage startups. About the Role We are seeking a technically proficient, business-minded Applied AI Engineer to help push the frontier of advanced AI with our strategic startup customers. You'll work with some of the most exciting AI startups in the world, guiding them through ideation, development, delivery, and scaling to accelerate and maximize the value of what they build on our platform. You will have the opportunity to work on the most novel and creative use cases being built on our API, serving as a critical partner in collecting and delivering high-fidelity product and model feedback internally. You will collaborate closely with Sales, Solutions Engineering, Applied Research, and Product teams, and you will report to the Startups Applied AI Lead. This role is based in our San Francisco or New York offices. 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: Partner closely with strategic startup customers as their technical thought partner to build novel applications on our API, helping them rapidly move from ideation to scale. Provide proactive guidance to maximize business impact and accelerate application development. Experiment and prototype alongside customers, demonstrating practical use cases. Contribute to open-source resources and scale the function by sharing knowledge, codifying best practices, and publishing useful resources. Synthesize and deliver valuable feedback to the Product and Research