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We believe that the way people interact with their finances will drastically improve in the next few years. We’re dedicated to empowering this transformation by building the tools and experiences that thousands of developers use to create their own products. Plaid powers the tools millions of people rely on to live a healthier financial life. We work with thousands of companies like Venmo, SoFi, several of the Fortune 500, and many of the largest banks to make it easy for people to connect their financial accounts to the apps and services they want to use. Plaid’s network covers 12,000 financial institutions across the US, Canada, UK and Europe. Founded in 2013, the company is headquartered in San Francisco with offices in New York, Washington D.C., London and Amsterdam. Plaid’s Product team builds the network that powers the future of financial services. Our mission is to unlock financial freedom for everyone through open finance. Product Managers at Plaid are curious, customer-obsessed, and move quickly to deliver value. They take ownership, sweat the details, and make sound decisions with imperfect information. As a Product Manager on the AI Foundations team, you will drive Plaid’s AI strategy by building the data and intelligence layer that powers smarter financial experiences. You will work across engineering, data science, and research to develop scalable AI systems—from core embeddings and representation learning to applied model integrations that enhance developer and consumer outcomes. This role is for an experienced PM who thrives at the intersection of AI and platform products. You are technically fluent, strategic, and execution-oriented. You enjoy turning advanced machine-learning capabilities into reliable, trusted infrastructure that scales across Plaid’s ecosystem. Responsibilities AI Platform Vision: Define the strategy, roadmap, and success metrics for Plaid’s core AI and data foundation, enabling smarter, more adaptive financial products across th

We believe that the way people interact with their finances will drastically improve in the next few years. We’re dedicated to empowering this transformation by building the tools and experiences that thousands of developers use to create their own products. Plaid powers the tools millions of people rely on to live a healthier financial life. We work with thousands of companies like Venmo, SoFi, several of the Fortune 500, and many of the largest banks to make it easy for people to connect their financial accounts to the apps and services they want to use. Plaid’s network covers 12,000 financial institutions across the US, Canada, UK and Europe. Founded in 2013, the company is headquartered in San Francisco with offices in New York, Washington D.C., London and Amsterdam. Plaid’s Product team builds the network that powers open finance. We unlock financial freedom by enabling innovation, reliability, and customer success. Our PMs are curious, fast-moving, and customer-obsessed, making intuitive, reliable experiences that help people thrive financially. As a Product Manager at Plaid, you’ll define and drive products that help millions connect, move, and manage their money. You’ll partner with engineering, design, and go-to-market teams to translate customer needs into impactful, high-quality products. This role suits someone early in their PM career who loves to learn, collaborate, and build meaningful solutions that improve financial lives. Responsibilities Own a product area: define problems, write clear requirements, and drive execution with cross-functional teams. Shape the roadmap using data, feedback, and market signals. Collaborate with Engineering and Design to deliver intuitive, high-quality experiences. Use metrics and user insights to measure and improve outcomes. Communicate clearly, align stakeholders, and guide decisions. Act with a founder mindset–simplify, move fast, and embrace feedback. Learn continuously and help raise the bar for Plaid’s products a

We believe that the way people interact with their finances will drastically improve in the next few years. We’re dedicated to empowering this transformation by building the tools and experiences that thousands of developers use to create their own products. Plaid powers the tools millions of people rely on to live a healthier financial life. We work with thousands of companies like Venmo, SoFi, several of the Fortune 500, and many of the largest banks to make it easy for people to connect their financial accounts to the apps and services they want to use. Plaid’s network covers 12,000 financial institutions across the US, Canada, UK and Europe. Founded in 2013, the company is headquartered in San Francisco with offices in New York, Washington D.C., London and Amsterdam. Plaid’s Product team builds the network that powers open finance. We unlock financial freedom by enabling innovation, reliability, and customer success. Our PMs are curious, fast-moving, and customer-obsessed, making intuitive, reliable experiences that help people thrive financially. As a Product Manager at Plaid, you’ll define and drive products that help millions connect, move, and manage their money. You’ll partner with engineering, design, and go-to-market teams to translate customer needs into impactful, high-quality products. This role suits someone early in their PM career who loves to learn, collaborate, and build meaningful solutions that improve financial lives. Responsibilities Own a product area: define problems, write clear requirements, and drive execution with cross-functional teams. Shape the roadmap using data, feedback, and market signals. Collaborate with Engineering and Design to deliver intuitive, high-quality experiences. Use metrics and user insights to measure and improve outcomes. Communicate clearly, align stakeholders, and guide decisions. Act with a founder mindset–simplify, move fast, and embrace feedback. Learn continuously and help raise the bar for Plaid’s products a

What you’ll do Design and implement secure cloud pipelines that ingest very large scan datasets (multi-terabyte), reliably and resumably. Build orchestration for GPU-accelerated reconstruction and analysis with strong retry semantics, idempotency, and cost controls. Define end-to-end data lifecycle for medical imaging: raw vs intermediate vs derived artifacts, retention policies, and reproducibility. Implement security + compliance primitives appropriate for HIPAA/PHI: encryption in transit/at rest, key management, least privilege, audit logs, and access reviews. Build operational tooling: monitoring, alerting, runbooks, and incident-driven improvements for a growing device fleet. What we’re looking for Strong experience with cloud batch/queueing/orchestration, storage systems, and data pipeline reliability. Experience shipping production systems that handle large data volumes and failure-prone networks. Practical security mindset (least privilege, secrets, audit logging) and comfort operating in compliance-constrained environments. Useful experience Building reliable data pipelines at scale (queues/orchestration, resumable uploads, GPU batch execution) with strong observability. Security + privacy by default: encryption, least-privilege access, auditing, and practical HIPAA/PHI guardrails. Owning the “boring” backend details that keep a lean team moving: schemas/migrations, cost controls, retries, and runbooks. Understanding compute tradeoffs across hardware options, and specifying appropriate cloud resources.

About the Team OpenAI for Financial Services is part of OpenAI's Verticals organization, which focuses on accelerating the economy and knowledge work. We build AI products for financial institutions and the professionals who power them, from investment bankers and research analysts to investors and other financial services teams. We combine OpenAI's models, financial data, and enterprise knowledge to help professionals research companies, analyze markets, and produce high-quality work in the tools they use every day. We're a small, entrepreneurial team working closely with customers and partners across research, product, design, engineering, and go-to-market to bring new capabilities from idea to production. About the Role We're looking for full-stack engineers to build new, AI-native products on top of ChatGPT Work and Codex. This is zero-to-one work: you'll help define how financial professionals research, analyze, and make decisions alongside AI. You'll own the experience across the stack, work directly with customers to understand their workflows, and collaborate across OpenAI to turn new model capabilities into products that professionals can trust. Your work will shape how some of the world's largest financial institutions adopt AI and how financial knowledge work gets done. In this role, you will: Build a new financial services app within ChatGPT Work and Codex, creating intuitive AI-native experiences for company research, financial analysis, document review, and professional work products. Develop new product experiences around enterprise memory that learn from an organization's knowledge, workflows, and context, and adapt to how its teams work. Develop the APIs, services, and integrations required to connect user experiences with financial data providers, enterprise systems, and OpenAI's models. Work closely with product and design to turn ambiguous customer problems into polished, useful, and reliable products. Work directly with financial institutions to

About the Team The GTM Intelligence Solutions team builds the data and decision systems that help customer-facing teams take the right action at the right time. We combine product telemetry, commercial data, customer context, and field activity to identify account health and opportunity, recommend actions and use cases, deliver intelligence through field-facing products and agent workflows, and measure what happens next. We’re looking for a Data Scientist to help build the next generation of GTM intelligence at OpenAI. You will own a flexible portfolio of high-impact decision data products and work closely with Technical Success and other GTM teams to ensure the work drives better decisions. About the Role As a Data Scientist on GTM Intelligence Solutions, you will define and build the intelligence systems that help customer-facing teams prioritize accounts, identify risks and opportunities, choose interventions, and understand what worked. You will set the roadmap and methodology, build canonical features, ship reliable production workflows, monitor quality and adoption, and improve the systems using field feedback and business outcomes. This role combines hands-on technical depth with strong product and business judgment. You should be as comfortable writing production Python and advanced SQL, defining durable data contracts, and operating decision products as you are evaluating a ranking approach or designing an experiment. You will personally ship reliable first versions and partner with Analytics Engineering and Data Engineering when work requires shared infrastructure or additional scale. In This Role, You Will Set the roadmap and methodology for GTM intelligence and decision products, using deep stakeholder discovery to probe beyond stated requests, uncover the underlying decisions, workflows, constraints, and measures of success, and translate them into measurable systems. Own the full lifecycle of intelligence products, including feature definition, methodo

About the Team GTM Growth Engineering builds AI-native products that help OpenAI's go-to-market and B2B marketing organizations scale with greater speed, intelligence, and operational effectiveness. We apply OpenAI models to real business workflows and build the systems that make those applications useful and dependable: customer context, agent behavior, feedback, evaluation, experimentation, and appropriate human oversight. Our work brings together software engineering, applied AI, product, data, and GTM operations. We measure success through the quality of customer engagement, pipeline, conversion, and the effectiveness of our sales and marketing teams. About the Role We're looking for an Applied AI Engineer to build production systems that help AI-powered go-to-market workflows improve over time. You will connect agent behavior, customer and operator feedback, evaluation, experimentation, and business outcomes to make these systems more effective, reliable, and responsive to evolving customer needs. This is a deeply technical, cross-functional role with end-to-end ownership of the agent improvement loop: understand production behavior, identify failure modes, improve how the system decides or acts, and validate the resulting impact. You will partner with Engineering, Product, Data Science, Sales, and B2B Marketing to turn real-world signals into safer, more effective agent behavior and measurable improvements in customer engagement, conversion, qualified pipeline, and team productivity. In this role, you will: Own the production improvement loop across agent behavior, customer and operator feedback, evaluation, experimentation, and verified business outcomes. Instrument agent workflows so model interactions, tool use, decisions, failures, human edits, and downstream outcomes can be understood in context. Define meaningful quality standards, representative evaluation datasets, regression coverage, and production monitoring for real GTM workflows. Investigate why a

About the Team The B2B Marketing team is responsible for helping businesses understand, adopt, and get value from OpenAI’s products. B2B marketing is a major and growing priority for OpenAI as we scale our work with companies, developers, and institutions around the world. About the Role Within B2B Marketing, Demand Generation builds the integrated, full-funnel engine that connects audience insights, content, field and digital experiences, paid media, lifecycle, and sales follow-through to qualified pipeline. We partner closely with Sales, Partnerships, Product Marketing, Communications, Creative, Web, RevOps, Analytics, and regional teams to create a cohesive customer experience and scale what works. We’re looking for a Senior Lifecycle Strategist to define how prospects and customers move through personalized, signal-driven journeys across the B2B lifecycle. You’ll own lifecycle strategy, audience and journey architecture, testing priorities, and performance recommendations while partnering closely with a Lifecycle Marketing Manager on build and delivery. Initially, the role will focus on prospect nurture, database activation, and sales handoff; over time, it will help expand our lifecycle capabilities across adoption, cross-sell, upsell, retention, and re-engagement. In this role, you will: Define the B2B lifecycle strategy, journey architecture, audience framework, communication principles, and roadmap across prospect and customer stages. Design nurture and activation programs that respond to fit, persona, segment, product interest, engagement, intent, and sales status rather than relying on one generic journey. Partner with RevOps, Data, Web, SDR, Sales, and Product teams to establish reliable triggers, scoring inputs, routing logic, suppression rules, exits, and service levels. Work closely with the Lifecycle Marketing Manager to translate strategy into clear program requirements, content needs, build plans, QA standards, and launch sequencing. Own the lifecyc

About the Team The Recursive Self-Improvement (RSI) team works across research, engineering, product, and infrastructure to build AI systems that accelerate and ultimately conduct high-quality research at OpenAI. We work to automate real research workflows and improve research productivity by building systems and feedback loops, designing evaluations, and training models to develop missing capabilities. Our work spans the full lifecycle of model training, evaluation, and deployment to help researchers move faster and tackle increasingly ambitious problems. About the Role We’re hiring research scientists , research engineers , and AI systems engineers to work on automating research at OpenAI. This role is based in San Francisco, CA. In this role, you will: Design evaluations for research judgment, hypothesis generation and testing, and long-horizon experiment execution. Turn real research workflows and model failures into data and evaluation flywheels. Improve model research capabilities through agent harnesses, synthetic data, RL environments, and model training. Build and maintain safe, reliable integrations between our models and OpenAI’s research infrastructure. Develop research agents, experiment-orchestration systems, and sandboxed runtimes that support real research workflows. Create metrics and economic models to understand RSI’s current and future effects on research productivity, model capabilities, and the safety of internal deployments. This is a high-ownership role for researchers and engineers who thrive in ambiguity, move fluidly between research and implementation, and turn emerging opportunities into rigorous, reliable, scalable results. You might thrive in this role if you: Have research or engineering experience across LLM training, model evaluations, agent systems, synthetic data, research infrastructure, or large-scale distributed systems. Are a strong generalist who can move between open-ended research and practical implementation, turning ambig

About the Team API Agents builds the shared agent harness, tools, and infrastructure that turn OpenAI’s frontier models into systems that can reliably complete real work. We carry the capabilities behind Codex into a much broader set of products and workflows across software engineering, research, finance, healthcare, enterprise operations, and more. Our work spans search and connected context, computer use, memory, delegation and multi-agent coordination, and safe execution. Sitting at the intersection of Research, Codex, infrastructure, and applied product teams, we build reusable agent capabilities that compound across the ecosystem. About the Role We are looking for an experienced backend software engineer to build the core systems behind the next generation of agents. You will design reliable services and abstractions that help agents find the right context, use tools and computers, retain knowledge, coordinate over long-running workflows, and take action safely. The role combines deep backend and infrastructure work with strong product judgment, with opportunities to work across agent runtimes, orchestration, search, execution environments, identity and permissions, observability, and evaluations. This is software and systems engineering rather than model training: success comes from strong backend fundamentals, high agency, and the ability to turn fast-moving research capabilities into dependable production primitives. In this role, you will: Design, build, and operate the shared agent harness and backend infrastructure that power long-running, high-value workflows across OpenAI and third-party products. Build reusable capabilities across search and connected context, computer use, memory, tool execution, delegation, subagents, and multi-agent orchestration. Establish the foundations agents need to operate safely in production, including secure execution environments, identity and permissions, observability, evaluations, reliability, and cost and latency effi

About the Team The compute infrastructure team runs the GPU fleet and large-scale compute clusters that serve the models backing ChatGPT and the API, while also supporting training workloads for our next generation models. We operate a large, modern GPU fleet and provide a unified platform for other OpenAI teams to seamlessly run production Applied AI and Research training workloads. We seek to learn from deployment and distribute the benefits of AI, while ensuring that this powerful tool is used responsibly and safely. Safety is more important to us than unfettered growth. About the Role You’ll own the hands-on and automation work that brings WAN, fiber, carrier, and cloud-interconnect circuits into service. Partner with network engineers, fiber providers, cloud service providers, colocation teams, and data-center technicians to move each connection from ordered and patched to verified, stable, and ready for handoff. You’ll own Layer 1 troubleshooting and circuit bring-up while building workflows that translate reliable system or model output into precise, approved technician actions, capture field feedback, and drive each connection to a green-port handoff. The right person combines strong physical-networking judgment with practical automation skills: patch-panel and port mappings, optics and light levels, provider coordination, structured operational data, API or scripting workflows, and human-in-the-loop LLM tooling. Responsibilities Own Layer 1 activation and restoration for carrier circuits, dark fiber, wavelengths, Ethernet handoffs, and dedicated cloud interconnects across data centers and points of presence. Reconcile complete A-side/Z-side as-builts: circuit IDs, LOAs/CFAs, carrier demarcations, MMR/ODF/MDF and patch-panel positions, fiber pairs, cross-connects, optics, and device ports. Investigate no-light, low-light, wrong-port, link-flap, and error-rate issues across providers and CSPs; isolate continuity, dirty connectors, polarity, incorrect patching

About the Team OpenAI's mission is to ensure that AGI benefits all of humanity. The Business Systems team helps make that mission possible by building the internal products and platforms that allow OpenAI to operate with speed, reliability, and care. We build internal applications and workflows for Finance and Supply Chain. Our work spans product discovery, React and TypeScript interfaces, Python services and APIs, data models, workflow orchestration, enterprise integrations, and the systems that connect people to systems of record. We work directly with the people who use these products and care about correctness, permissions, auditability, and production reliability. Examples of our work include building an integration platform for supply chain integrations, integrations with Oracle Fusion and Zip, contract intelligence applied to B2B revenue recognition, and Temporal-based agentic workflows for credit checks, duplicate bank detection, and invoice triaging. We turn these efforts into reusable patterns that can support many workflows, rather than one-off automations. About the Role We are looking for Product Engineers to build internal applications end to end. This role spans product discovery, user experience, frontend, backend services, data models, workflow orchestration, and integrations with order management, fulfillment, and supply chain systems. You will take a problem from a first conversation with a Finance or Supply Chain partner through design, implementation, rollout, and production support. Strong candidates combine product judgment with engineering depth. You should be comfortable moving between a React interface, a Python API, a durable workflow, and an integration with an enterprise system. You should be able to ship a useful first version quickly while building the foundations for reuse, security, and long-term maintainability. Direct AI experience is helpful, but the core requirement is strong product engineering judgment and reliable execution. I

About the Team The Personal AGI team is responsible for training and improving pre-trained models to be deployed into ChatGPT, the API, and potential future products. In the Model Experience team, we shape the default character and behavior of ChatGPT: how the model communicates, responds to users, uses its capabilities, and behaves across different contexts and languages. Our goal is to make every interaction with ChatGPT thoughtful, helpful, and trustworthy. We take an opinionated view of what good human–AI interaction should look like, then turn that vision into real model behavior through human data, evaluations, reward models, and post-training. Our work sits at the intersection of research, product, and model design. We partner closely with teams across OpenAI to conduct research and ensure our models are thoughtful, safe, reliable to serve millions of users. About the Role As a Research Engineer / Scientist, you will research and develop improvements to our models. Our team works in research areas combining reinforcement learning and products. We're looking for individuals with strong ML engineering skills and research experience, especially with novel and highly capable models. An ideal candidate is passionate about product-driven research and the quality of human-AI interaction. This role is based in San Francisco, CA. We use a hybrid work model of 3 days in the office per week and offer relocation assistance to new employees. In this role, you will: Own and pursue a research agenda to improve model capability and performance. Collaborate closely with the other research and product teams, allowing customers to optimize their own models. Build robust evaluations for tracking modeling improvements. Design, implement, test, and debug code across our research stack. You might thrive in this role if you: Have a deep understanding of machine learning and machine learning applications. Have good judgment about model behavior and can communicate this judgment effec

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 Our Robotics team is focused on unlocking general-purpose robotics and pushing towards AGI-level intelligence in dynamic, real-world settings. Working across the entire model stack, we integrate cutting-edge hardware and software to explore a broad range of robotic form factors. We strive to seamlessly blend high-level AI capabilities with the constraints of physical systems to improve people's lives. About the Role We are seeking a lead thermal simulation engineer to help accelerate the design and development of next-generation robotic systems through modeling, simulation, and analysis. You will work closely with mechanical, electrical, controls, and robotics engineers to evaluate designs before hardware is built, identify risks early, and guide critical architecture decisions. This role spans structural and thermal analysis and design across robotic subsystems including actuators, mechanisms, structures, electronics, and integrated systems. You will develop simulation workflows that improve engineering velocity, increase confidence in design decisions, and help us build more capable, reliable, and manufacturable robotic platforms. This role is based in San Francisco, CA. This role will be expected to be in office 4 days per week and offer relocation assistance to new employees. In this role, you will: Perform thermal simulations to assess heat generation, cooling strategies, thermal interfaces, and system-level thermal performance Partner with mechanical, electrical, and controls engineers to influence design decisions early in development Build simulation models to evaluate robotic actuators, transmissions, mechanisms, structures, soft goods, and integrated assemblies Correlate simulation results with physical testing and develop methodologies to improve model accuracy Support architecture trade studies by evaluating design concepts before hardware is built Develop simulation workflows, standards, and best practices that scale across the robotics o