Content Support Engineer in San Francisco

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 builds the data, environments, graders, training methods, and feedback loops that shape what OpenAI’s next agents can do and what they are like to work with, then carries those improvements through major training runs and into products used by people every day. About the Role As a member of the Agent Post-training Personality team, you will help make OpenAI’s agents exceptional collaborators. You will study what makes an agent thoughtful, clear, perceptive, appropriately proactive, and genuinely easy to work with, then translate those insights into evals, training data, reward signals, and model improvements. We use “personality” to mean much more than writing style or general likability. It includes whether an agent understands what the user is trying to accomplish, communicates with good judgment, adapts to context, asks useful questions, handles disagreement honestly and takes initiative at the right moments. The goal is to create a strong, tasteful default that can adapt to different people and situations. This work combines behavioral research, product thinking, research and communication taste. You will collaborate with product teams, human experts, and researchers across post-training and pretraining to ensure that improvements survive the full trai

About the Team OpenAI’s mission is to build safe artificial general intelligence (AGI) that benefits all of humanity. Achieving this requires bringing the world’s most exceptional talent under one roof to push the boundaries of what’s possible. Our Research Recruiting team plays a critical role in this effort. We are an embedded part of the research organization—working side by side with our research staff to deeply understand evolving priorities, build trust, and strategically shape the future of OpenAI’s talent. About the Role We are looking for a highly strategic recruiter to work closely with the Head of Research Recruiting on a small set of unusually important, high-touch searches and candidate relationships. This role will focus on exceptional talent who does not move through a standard recruiting process: highly visible researchers, technical leaders, operators, and other special-interest candidates where timing, discretion, market intelligence, and tailored engagement matter as much as process execution. This is not a conventional req-based recruiting role. You will help identify where the market is moving, develop intelligence on top talent and competitor activity, translate that intelligence into action, and orchestrate bespoke recruiting strategies for candidates who require a more nuanced path into OpenAI. You should be able to translate these signals and states into clear, actionable advice for leaders and then make it happen. In this role, you will: Partner directly with the Head of Research Recruiting and research leadership team to define priority talent targets and shape bespoke engagement strategies. Build and maintain deep market intelligence across frontier AI, research, engineering, and adjacent talent ecosystems, including competitor movement, candidate motivations, and relationship context. Proactively identify, map, and cultivate exceptional high-profile talent before there is a formal or standard hiring process attached. Translate weak signa

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 OpenAI's Research Data Team exists to accelerate the evaluation, safety and capabilities of our models and products. Made up of technical operators and software engineers, we design the methods in which we acquire and create data. About the Role As a Research Program Manager (RPM), Data Acquisition, you will partner with research, engineering, and operations to design and implement pragmatic solutions for acquiring data. You will be a key interface between our research roadmap and external data offerings. This role is based in our San Francisco HQ and will be part of a team of RPMs pushing the frontier of data acquisition. In this role, you will: Partner deeply with research: Work with researchers to scope data needs, define success criteria, and translate priorities into clear execution plans. Shape the data acquisition pipeline: Identify, evaluate, and advance high impact data opportunities - balancing research value, feasibility, quality, and responsible execution. Unblock yourself: Move work forward even when the path is unclear — using technical judgement, creative problem solving, and scrappy execution to make progress while longer-term solutions are still forming. Build lightweight systems and visibility: Use SQL, Python, dashboards, and simple tooling to track performance, quality, and blockers. Drive technical roadmaps: Collaborate with engineers to enhance data platforms, resolve blockers, and ensure security best practices such as access management. Scale your impact: Equip vendors and internal teams with the context, standards, and operating rhythms needed to focus on the most important problems. You’ll thrive in this role if you: Are proficient in SQL and Python for analysing datasets, querying databases, building dashboards, and generating actionable insights. Are comfortable using APIs, automation, and AI tools such as Codex to accelerate workflows, remove manual overhead, and upskill quickly in unfamiliar technical areas.Experience sou

About the Team Safety Systems works to ensure OpenAI’s most capable models can be developed and deployed responsibly. Our work spans evaluations, safeguards, red teaming, deployment decisions, and the systems that help OpenAI understand and reduce risk as models become more capable and widely used. Within Safety Systems, the Trustworthy AI team is growing its safety transparency function: a practice focused on helping external audiences understand OpenAI’s technical safety work with greater clarity, rigor, and continuity. We create and improve the public artifacts that explain how our systems are evaluated for safety, what safeguards we build, what decisions we make, and where uncertainty remains. This work includes system cards, the Deployment Safety Hub, safety-related blogs, public governance documents, and other outputs that communicate technical safety topics to external audiences. It also includes building new ways to make technical safety information easier to understand, navigate, and use—including AI-assisted workflows, data visualizations, and interactive tools that make complex technical work more legible over time. About the Role We are looking for a Safety Transparency Editor to own the editorial quality of key safety transparency artifacts and systems. This is a hands-on role for someone who can write crystal-clear, pitch-perfect explanations of the hardest and highest-stakes technical safety topics that OpenAI tackles, and who can lean into AI to build systems that help the broader organization do this work better. Your core responsibility is to shape and execute how our technical safety work is externally communicated: identifying the narrative thread, exercising judgment about which details matter, determining where additional context, explanation, or supporting evidence is needed, translating complexity without sacrificing precision, and helping external audiences understand both the safety measures we’ve taken and the uncertainties that remain. To

About OpenAI OpenAI is dedicated to ensuring that artificial general intelligence (AGI) benefits all of humanity. Our mission requires building not only world-class AI models, but also the infrastructure that enables those models to be deployed reliably, efficiently, and at global scale. As demand for AI continues to grow, we are expanding the ways OpenAI can bring high-performance inference capacity online across a diverse hardware ecosystem. About the Team The GPT Infrastructure team builds software that turns advanced inference and optimization research into production products. One focus is enabling strategic infrastructure partners and accelerator vendors to qualify and onboard new compute without a bespoke porting and optimization effort for every hardware platform. We build the control planes, APIs, secure partner-side execution environments, evaluation systems, artifact pipelines, and operational tooling that make these workflows repeatable and trustworthy. The work sits at the intersection of distributed systems, AI inference, compilers and runtimes, performance engineering, security, and external partnerships. About the Role We are seeking an experienced systems generalist who can work comfortably across the stack to help build an automated inference optimization platform. Given a workload, target hardware profile, compiler and runtime context, and a trusted verifier, the system runs durable optimization campaigns that generate, compile, execute, grade, and improve candidate kernels, runtime configurations, and serving-stack changes. You will design both the OpenAI-hosted control plane and the partner-side software that evaluates candidates on real accelerator hardware. The product must keep long-running workflows reliable, make performance results reproducible, and maintain clear trust boundaries around sensitive model and hardware information. This is a deeply cross-stack role, combining strong software engineering fundamentals with systems thinking and

About the Team The Codex Core Agent team builds the kernel of Codex. We own making the agent better, accelerating research, and making those improvements real in production for our users. That means working across the systems that make Codex actually function as an agent in the real world: the production performance envelope around tokens, latency, reliability, cost, and capacity; the core execution loop and interfaces that turn models into useful behavior; the shared infrastructure that enables other teams to build on Codex; and the feedback loops that turn real-world usage into better models and better agent behavior over time. About the Role We’re looking for applied AI engineers to help bring Codex agents from impressive demos to dependable tools. This role is about improving agent performance on real software engineering tasks and closing the gap between research capability and real-world usefulness. You’ll work closely with research, infrastructure, and product to ensure agents are not just powerful, but useful, steerable, and reliable in practice. The job is not only to improve model behavior in isolation, but to turn those improvements into measurable gains in solve rate, usefulness, and economic value for users. What You’ll Do Design and iterate on agent behaviors across real-world coding tasks and long-horizon workflows. Work closely with research to develop and run evals to measure agent performance, regressions, failure modes, and edge cases. Improve performance through prompting, tool-use strategies, context construction, and model-facing experimentation. Analyze failures in production and systematically improve robustness and reliability. Build feedback loops and data systems that get better real-task data into evaluation and research. Work with product teams to shape user-facing agent experiences and the interfaces the agent depends on. Help define what “good” looks like for agents completing complex tasks end-to-end. You Might Be a Good Fit If You Ha

About the Team The Integrity team builds the systems OpenAI uses to understand, prevent, and respond to misuse across our products. We partner with Product, Policy, Safety Systems, User Operations, Security, Legal, Privacy, OpenAI for Government, and research teams to turn policy and threat models into product controls, review workflows, measurement systems, and enforcement paths. About the Role We are hiring a Product Manager to own Integrity's product strategy for sensitive deployments: contexts where model capability, customer or deployment setting, privacy constraints, and misuse potential make the operating bar unusually high. This includes government and other high stakes deployments, regulated or high-trust enterprise contexts, zero data retention and privacy-constrained environments, and agentic workflows where harm can unfold across many steps rather than a single prompt. The Sensitive Deployments PM will focus on high-consequence use cases relevant to government deployments and broader deployment-readiness questions for high-risk domains (e.g., healthcare), while building reusable Integrity capabilities for agentic detection and enforcements in these environments. This position is based in San Francisco, CA, with relocation assistance available. In this role, you will: Own the roadmap for sensitive deployment Integrity controls and readiness criteria. Define how we measure residual risk, decision quality, review quality, and mitigation effectiveness. Build agentic investigation and context-assembly workflows for complex, multi-step misuse patterns. Partner with Policy, Legal, Privacy, Safety Systems, User Ops, Government, and product teams to build shared capabilities. Create launch and deployment playbooks for sensitive customer, capability, and product contexts. You might thrive in this role if you: Have shipped complex product systems in AI, integrity, trust and safety, security, privacy, risk, government, regulated enterprise, or AI safety. Can turn am

Who We Are Notion is the collaborative AI workspace where teams and agents think together . We're building one place where your knowledge, projects, meetings, and AI tools live side by side, so work is faster, clearer, and less fragmented. Millions of individuals, small teams, and large companies run their work on Notion. Notinos (our employees) are customer zero in bringing this future of work to life. We care about craft, building things that last, and the belief that great work is still fundamentally human. Our goal isn’t to ship the next feature. Each and every team of Notinos is working to set the standard for how humans work together in the AI era. From building a business’s system of record to making and managing AI agents to automating away the busy work, we care deeply about giving our customers more time for their life’s work. About the Role: We’re seeking a Rolling User Researcher (Rolling UXR) to deliver fast, high-signal insights that improve Notion’s product experiences across Product, Design, and Engineering (EPD)—including AI-powered workflows like Notion custom agents and chat. This is a tactical, high-velocity role: you’ll run lightweight usability and concept tests on a steady cadence, identify what’s not working, and help teams translate feedback into concrete product changes. Besides conducting research, you’ll also build and scale a program that makes it easy for product teams to submit testing requests and easy for you to recruit participants, along with a prioritization framework that helps you focus on the most important work. This role is ideal for a researcher who loves the craft of moderated testing, can context-switch across many teams, and thrives in a “many small studies” model. This role can be based in either San Francisco or New York City. We work from our offices on Mondays, Tuesdays and Thursdays (our Anchor Days) because we do our best thinking and building together in person. We’re looking for someone who’s excited to work along

Who We Are Notion is the collaborative AI workspace where teams and agents think together . We're building one place where your knowledge, projects, meetings, and AI tools live side by side, so work is faster, clearer, and less fragmented. Millions of individuals, small teams, and large companies run their work on Notion. Notinos (our employees) are customer zero in bringing this future of work to life. We care about craft, building things that last, and the belief that great work is still fundamentally human. Our goal isn’t to ship the next feature. Each and every team of Notinos is working to set the standard for how humans work together in the AI era. From building a business’s system of record to making and managing AI agents to automating away the busy work, we care deeply about giving our customers more time for their life’s work. About The Role Build the most advanced AI Meeting Notes product — and expand it into broader “AI data capture” features that help teams turn conversations into durable context, tasks, and knowledge. Our mission is to 10x the rate of business context & data that enters Notion — optimized for agents — so teams get superhuman memory across workstreams and customers. Notion workspaces that use AI Meeting Notes already enter 6x more data on a daily basis, so we’re well on our way. What You'll Achieve Ship end-to-end product experiences across capture → transcript → summary → follow-ups (full-stack ownership). Make meeting & data capture feel effortless and magical (e.g., speaker identification via audio waveforms, richer in-meeting UX, smarter organization). Improve summary quality that teams trust: structure, factuality, and citations that make downstream agents and humans more capable. Raise the bar on reliability & observability across the pipeline (SLOs, debugging workflows, incident response) for realtime systems. Build agentic meeting workflows that turn discussions into tasks, follow-ups, and organized knowledge — so “w

Who Are We? Postman is the world’s leading API platform, used by more than 45 million+ developers and 500,000 organizations, including 98% of the Fortune 500. Postman is helping developers and professionals across the globe build the API-first world by simplifying each step of the API lifecycle and streamlining collaboration—enabling users to create better APIs, faster. The company is headquartered in San Francisco and has offices in Boston, New York, Austin, Tokyo, London, and Bangalore - where Postman was founded. Postman is privately held, with funding from Battery Ventures, BOND, Coatue, CRV, Insight Partners, and Nexus Venture Partners. Learn more at postman.com or connect with Postman on X via @getpostman. P.S: We highly recommend reading The "API-First World" graphic novel to understand the bigger picture and our vision at Postman. The Opportunity We're hiring a Senior Software Engineer to own the implementation, inventory, monitoring, and performance of every third-party integration that touches our numerous web properties: conversion pixels, trackers, first- and third-party analytics, ABM, APIs, cAPIs, cookie consent banners, and whatever the next law or vendor throws at us next quarter. This is not a migration. It is not a zero-to-one build. It's a plane flying well, and the person you're backfilling left it in good standing with thorough notes. You'll be taking over the controls, tuning the instruments, and working through a backlog of challenging and interesting projects. If you're looking for a stepping stone to something else, this isn't it. If you want to deeply nerd out on analytics, engagement, performance, and the craft of getting third-party implementations right, read on. What You'll Do About 90% of your time will be spent on third-party integrations: implementing them, inventorying them, tracking them, monitoring their uptime and the veracity of their data streams, and optimizing their performance. These integrations are helping the business get

Who We Are Notion is the collaborative AI workspace where teams and agents think together . We're building one place where your knowledge, projects, meetings, and AI tools live side by side, so work is faster, clearer, and less fragmented. Millions of individuals, small teams, and large companies run their work on Notion. Notinos (our employees) are customer zero in bringing this future of work to life. We care about craft, building things that last, and the belief that great work is still fundamentally human. Our goal isn’t to ship the next feature. Each and every team of Notinos is working to set the standard for how humans work together in the AI era. From building a business’s system of record to making and managing AI agents to automating away the busy work, we care deeply about giving our customers more time for their life’s work. About the Role: Millions of people use Notion — and this number is increasing every day. That means millions of people trust us to deliver a fast, reliable, and secure experience, and we value this more than anything. We want to keep earning trust, while also continuing to amaze our users with the tools they can build in Notion. The AI Platform team is responsible for building the shared foundations that let Notion ship AI products quickly and operate them safely at scale. You’ll join a team of talented engineers focused on making speed and quality compatible: reliability and availability through provider changes, quality and correctness systems like evals and release gates, observability that makes failures explainable, and shared primitives for model integrations, context management, long-running actions, and cost/performance tradeoffs. Notion’s AI platform is vital to helping product teams move faster with production-grade guardrails as models, providers, and AI capabilities rapidly evolve. This role can be based in either San Francisco or New York City. We work from our offices on Mondays, Tuesdays and Thursdays (our Anchor Days)