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About the Team OpenAI’s Real Estate & Workplace (REW) team creates and operates the spaces, programs, and services that enable employees to do their best work. The team’s work spans real estate, workplace operations, design and construction, workplace experience, food programs, employee services, analytics, and global programs. As OpenAI’s presence in India grows, REW is building flexible, scalable, AI-native, people-centered workplaces that combine a consistent employee experience with strong operational and financial discipline and thoughtful adaptation to each location. Workplace & Location The location for this role is Mumbai . The role requires to be onsite 5 days per week, with regular travel among Mumbai, New Delhi, and Bangalore. About the Role We are looking for an experienced Senior Workplace Operations Manager to set the operational direction, define the workplace operating model, and lead workplace operations and employee experience across OpenAI’s locations in India, including but not limited to Mumbai, New Delhi, and Bangalore. This hands-on, country-level role combines strategic planning, senior stakeholder partnership, and day-to-day execution in a fast-changing environment. As OpenAI’s senior workplace operations owner in India, you will translate business and employee needs into a scalable, AI-native operating model and define the standards, priorities, and service outcomes that support the country’s growth. You will remain accountable for delivery through integrated facilities-management partners, contractors, and specialist vendors, while partnering closely with India business leaders, regional REW leadership, and colleagues across REW, Workplace Experience, Leasing, Design & Construction, Food, Corporate Security, IT, Finance, People, and Procurement. This is a senior individual-contributor role reporting to the Head of Workplace, JAPAC. You’ll be responsible for: Set and own the country-wide workplace operating model, service qualit

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 Trusted Computing and Cryptography is a core security team at OpenAI focused on deploying high-performance cryptography at scale, secure key management, and trusted hardware enclaves—from boot measurements to GPU confidential computation. As a Hardware Platform Security Architect, you’ll own hardware platform security at OpenAI. In this role, you will: Co-Architect Secure Silicon: Collaborate with cross-functional silicon teams (Silicon Design, DV, FW) and silicon partners (silicon test facilities, foundries) to develop secure silicon that meets the end-to-end system requirements. Co-Architect Secure Hardware: Collaborate with hardware vendors and cross-functional teams (kernel, compiler, infra) to design secure hardware that meets performance and security needs. Co-Architect Secure Systems: Architect and deploy systems using TPM2, Secure Boot, Nitro Enclaves, Intel SGX, AMD-SEV, and other secure hardware technologies. Drive Innovation: Engage with internal and external partners to align hardware innovations with OpenAI’s trusted computing and cryptographic requirements. You might thrive in this role if you have: 10+ years of industry experience in hardware security or hardware–software co-design. Proven expertise in deploying secure hardware systems at scale and integrating secure hardware primitives. Strong coding skills in Rust and/or C/C++, with proficiency in Python. Proven ability to collaborate across teams, architect solutions,

About the Team The Strategic Initiatives & Operations team is in need of a Technical Program Manager (TPM) to streamline our processes, including full safety governance and integration of various safety research and mitigations into our ChatGPT, API, and any frontier models. This role is critical for driving safe deployment of our new models, synthesizing inputs from multiple stakeholders, ranging across research, product, engineering, legal and policy, and ensuring all the risks are effectively and properly monitored, mitigated or resolved. About the Role As a TPM, you will be responsible for critical tasks ranging from tracking safety research progress and risk tables to overseeing the quality of human data campaigns – acting as the connective tissue to enhance the deployment of OpenAI’s safety system. Additionally, you will create and execute a compute roadmap for your team to ensure that our top priorities are resourced while taking advantage of new opportunities to make key safety research discoveries. Your primary focus will be to ensure our models are qualified for safe deployment. 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: Manage key risk areas and corresponding stakeholders. Keep track of a stack of existing and future mitigations for every major product and model deployment. Standardize the lifecycle of risk assessment, setting safety bars, consolidating inputs from multiple stakeholders across research, product, engineering, legal and policy, pre-launch safety reviews and post-launch followup. Manage pre-launch safety reviews. Share launch calendars and key safety practices and evaluations with our key parter (i.e. Microsoft). Develop comprehensive documentation for all the safety work, including metrics, evaluations, and progress tracking across multiple teams within OpenAI. Help with publishing and open sourcing safety

About the Team The Legal team is building the next generation of AI-powered products and experiences for the legal industry. We are exploring how advanced AI systems can transform legal workflows, improve access to information, and enable legal professionals and organizations to work more effectively. As a founding member of the Legal engineering team, you will help define the technical foundation for this new product area from the earliest stages. You’ll operate at the intersection of AI, product, and real-world legal workflows—identifying opportunities, building prototypes, and turning emerging ideas into scalable products that can create meaningful impact. We operate with a startup-like mindset inside OpenAI: small teams, rapid iteration cycles, and a willingness to explore bold ideas, learn quickly, and adapt based on user feedback. Our goal is to build products that meaningfully improve how legal professionals work while leveraging OpenAI’s cutting-edge models and infrastructure. About the Role As a Founding Full-Stack Software Engineer on the Legal team, you will help imagine, build, and scale new AI-powered products for the legal industry. You’ll work across the stack to design intuitive user experiences, build robust backend systems, and create the foundations for products used by legal professionals and organizations around the world. You’ll have significant ownership from the earliest stages—working closely with product, design, research, and go-to-market partners to understand customer needs, shape product direction, and deliver high-impact solutions. This includes rapidly prototyping new concepts, building production-quality applications on top of OpenAI’s platforms, and developing new technical approaches when existing systems are not sufficient. We’re looking for engineers who thrive in ambiguity, have strong product instincts, and enjoy building from 0→1. You should be comfortable moving quickly, making thoughtful technical decisions, and taking owner

About the Team The Consumer Devices team is building a new generation of AI-native experiences that bring OpenAI’s technology closer to people throughout their day. We are exploring how AI can become deeply integrated into the devices people already use—understanding context, helping users accomplish tasks, coordinating across apps and services, and making use of capabilities that are uniquely available on the device. Our work spans software, devices, platform technologies, and strategic partnerships. We operate as a highly collaborative, fast-moving team focused on turning ambitious ideas into reliable products that can reach people at significant scale. About the Role As an Android Systems Engineer , you will help define and build the Android platform capabilities behind the next generation of AI-powered consumer experiences. This is not a traditional application-only Android role. You will work across the Android stack, using system-level APIs, device capabilities, platform integrations, and OEM-specific functionality to create experiences that would be difficult or impossible to build as a conventional mobile application. You will help determine what becomes possible when an AI assistant is deeply integrated with a mobile device: how it understands what is happening, interacts naturally with the user, connects across apps and services, and helps complete multi-step tasks. You will work closely with product, design, AI, backend, platform, and external device partners to take new concepts from prototype to production. The role requires strong technical judgment, product intuition, and comfort navigating areas where the platform, APIs, and product patterns are still evolving. This role is based in San Francisco, CA. We use a hybrid work model of four days in the office per week and offer relocation assistance to new employees. In this role, you will: Build deeply integrated Android experiences: Design and implement product experiences that take advantage of Android

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 OpenAI is seeking to build an investigative capability for Secure Manufacturing & Stealth programs. The risk surface for unreleased products, prototypes, confidential hardware, infrastructure, supply chain, manufacturing, and launch-readiness efforts spans employees, vendors, suppliers, logistics partners, physical movement of assets, procurement records, manufacturing workflows, access systems, device telemetry, and adversarial collection. This role is intended to build and run investigations across that specialized environment. In this role, you will: Lead complex SMS investigations to proactively identify and mitigate risks to unreleased products, prototypes, confidential hardware, secure manufacturing programs, and launch-readiness efforts. Investigate unauthorized disclosure, suspected leaks, insider risk, supplier compromise, vendor misconduct, theft, diversion, tampering, counterfeiting, surveillance, adversarial collection, and suspicious activity involving sensitive programs. Connect digital evidence, physical access activity, supply chain records, manufacturing data, vendor behavior, employee activity, collaboration metadata, procurement records, shipping data, and OSINT into clear findings and risk-reduction actions. Conduct proactive threat hunting to surface early indicators of compromise, collection, leakage, or insider activity affecting sensitive programs. Develop investigative playbooks, evidence-handling standards,

About the Team Codex is OpenAI's software engineering agent. Codex Security extends that work into one of the most important product areas in AI: helping organizations find, validate, prioritize, and fix real vulnerabilities in the software they build and depend on. The Codex Cyber team is building the product and platform foundations for AI-native application security. This includes Codex Security product experiences, cloud-based security analysis, platform controls across Codex, customer deployment and support tooling, and infrastructure that helps security researchers and cyber models improve over time. The team is early, small, and growing quickly, with a mandate to move fast and hire exceptional builders. About the Role We are looking for software engineers first: strong full-stack or product-minded generalists who can own ambiguous product and platform problems end to end. Security experience is helpful, and security curiosity is important, but this is not a role for security specialists who only occasionally write code. The right person is an excellent builder who is excited to work in security and can turn complex research, product, and customer needs into reliable systems. You will work across user-facing product surfaces, developer workflows, backend services, security analysis pipelines, cloud infrastructure, and internal tooling. You may build features that make Codex Security more useful for application security teams, systems that scale cloud-based security analysis, platform controls that make agentic coding safer, or infrastructure that helps security researchers and models become more effective. You will collaborate closely with engineering, product, security research, infrastructure, and customer-facing partners as Codex Cyber becomes a major product and platform investment for OpenAI. In this role, you will: Build end-to-end product features for Codex Security, from developer-facing interfaces to APIs, backend services, and workflow tooling. Own a

About the Team OpenAI’s Cyber team works to make frontier AI a decisive advantage for defenders. The Cyber Blue Team is an operator-led group focused on turning real defensive problems into better models, useful products, safe Codex workflows, and integrations with the security tools defenders already use. Our ambition is simple: Raise attacker cost. Lower defender toil. Prove it by defending OpenAI; scale it through the ecosystem. We are not setting out to build another SIEM or autonomous SOC. We want to build the AI reasoning and workflow layer that helps security teams investigate threats, create and validate detections, improve their controls, and respond with greater speed and confidence. About the Role We are looking for a Product Manager to help build a new generation of AI-powered cyber defense products. You will work closely with security practitioners, researchers, engineers, designers, internal security teams, customers, and technology partners to turn emerging model capabilities into products that solve meaningful defensive problems. This is an early-stage product role. The work will span product discovery, prototyping, evaluation, development, launch, and iteration. You will help the team identify where AI can create the most value for defenders and translate those opportunities into clear, usable, and trustworthy product experiences. Initial areas of focus may include: Detection engineering and detection-content development Threat hunting and investigation Security validation and control testing AI-agent and MCP runtime defense Integrations with security platforms and enterprise workflows Safe, governed assistance for incident response The specific roadmap will continue to evolve based on model progress, practitioner needs, internal learnings, and customer feedback. In This Role, You Will Work with security practitioners to understand high-value defensive workflows, recurring pain points, and opportunities for AI to materially improve outcomes. Help sh

About the team Special Situations at OpenAI is the company’s commercial engine for its most complex and consequential opportunities. The team operates where new verticals, partnership models, and customer motions must be invented, before a repeatable GTM or delivery playbook exists, by bringing together product, research, engineering, and GTM leaders around a single outcome. Many of these efforts begin as “new bets” (new verticals, new customer motions, new partnership models) and mature into repeatable ways of working that shape how OpenAI operates at scale. The team acts as a force-multiplier for the company by accelerating decision-making, aligning stakeholders, and converting complex opportunities into durable results. About the role We’re hiring a Deal Lead, Special Situations to help stand up new vertical bets, with a particular focus on partnership development in the semiconductor industry. You will identify where AI can create step-change value for semiconductor companies and ecosystem partners, originate and shape multi-project programs with FDEs and applied researchers, package them into compelling commercial proposals, and execute creative, complex partnerships while keeping executives and cross-functional teams tightly aligned. This role is especially focused on building strategic partnerships in semiconductors. You do not need to be a technical engineer, but you do need to be able to speak the language of the semiconductor industry, build credibility quickly with technical and business stakeholders, and come up the learning curve fast on industry dynamics, workflows, and constraints. A key part of the role is crafting deals with semiconductor companies that demonstrate our unique competitive advantages. To do this effectively, you will need to develop a strong point of view on the market, understand the competitive landscape, and clearly articulate OpenAI’s differentiated value and strategic advantage. Special Situations owns the overall success of each

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 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 Training Runtime builds the distributed systems that power OpenAI's largest model training runs - most recently GPT-5.5! The Data Movement area owns the infrastructure that keeps training jobs supplied with the right data at the right time, and keeps model state moving safely and efficiently across large clusters. Our work spans machine learning systems, distributed storage, high-throughput data loading, reliability engineering, and developer experience. Success means researchers can move quickly while training runs remain fast, reproducible, debuggable, and resilient at scale. About the Role We are looking for a deeply hands-on Technical Lead Manager to own datasets throughout our training infrastructure. This person will set the direction for how training jobs read data: the APIs, storage contracts, versioning model, benchmarks, debugging tools, and reliability guarantees that make data access consistent across current and future training frameworks. You will begin as the primary technical owner for dataset reads, working directly in the code while aligning researchers, training framework owners, storage teams, and infrastructure partners around a durable platform. The problem is deceptively hard at frontier scale: make enormous, heterogeneous datasets easy to consume, correct across distributed workers, observable when something goes wrong, and flexible enough to support pretraining, reinforcement learning, and multimodal training. In this role, you will Design and build a unified dataset read platform for multiple current and future training frameworks. Define dataset APIs, storage-format expectations, registration/versioning, and migration paths that make data access reproducible and maintainable. Build reliability into the read path, including stateful iteration, caching, fast restart, recovery, and clear operational contracts. Build terminal and web-based visualizers that let teams inspect text, multimodal, and reinforcement learning data late