Data Validation Liquidity in San Francisco

About the Team OpenAI Finance ensures the organization is positioned for long-term success as we pursue our mission. The Revenue team plays a critical role in enabling OpenAI to scale its commercial offerings—overseeing billing operations, deal desk, revenue systems, and revenue accounting. We work cross-functionally with Technical Revenue, Finance Data, and Revenue Systems teams to support complex commercial arrangements, improve operational efficiency, and maintain financial integrity. About the Role As a Revenue Accounting Manager, you will own revenue accounting processes for consumption and usage-based revenue recognition while helping implement and maintain the systems, data flows, and accounting rules that support accurate financial reporting. You will serve as a key execution partner on onboarding new revenue streams, Fusion Accounting Hub rule updates, translating accounting requirements into expected journal entries, source-to-general-ledger mappings, user acceptance testing, data validation, and controlled operating processes. We’re looking for a hands-on revenue accounting owner who combines strong close discipline with systems and data fluency, independently coordinates cross-functional implementation work, and strengthens the accounting infrastructure supporting OpenAI’s growth. 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 the monthly close for usage-based revenue and payment processor accounting, including journal entries, reconciliations, variance analysis, controls, and supporting documentation. Prepare and review revenue-related journal entries while understanding the underlying transaction lifecycle, accounting methodology, billing arrangements, source data, and expected financial reporting outcomes. Perform reconciliations for key revenue accounts, investigate discrepancies, and drive issues to resolution. Own flux

About the Team OpenAI is building the infrastructure foundation for the next generation of AI. The Data Center Engineering team defines the strategy, reference architectures, technical requirements, and delivery standards for the large-scale data centers that support OpenAI research, products, and infrastructure partners. As a Data Center Infrastructure Electrical Engineer, you will help define, validate, and scale the electrical power systems that support high-density AI compute. You will translate evolving compute requirements into practical facility and rack-power architectures, evaluate new technologies and vendor solutions, and drive technical decisions across design, manufacturing validation, construction, commissioning, deployment, and operations. This role is best suited for a senior hands-on engineer with deep experience in mission-critical power systems, strong judgment under ambiguity, and the ability to connect facility infrastructure, hardware requirements, controls, telemetry, reliability, and operations. About the Role We are seeking a senior electrical infrastructure engineer to lead the development of reliable, scalable, and efficient power architectures for high-density, liquid-cooled AI data centers. The ideal candidate has strong practical experience with critical electrical systems at data centers or comparable industrial scale, including medium-voltage and low-voltage distribution, utility interfaces, backup power, UPS and battery systems, rack power delivery, grounding, protection, controls, and monitoring systems. You should be comfortable moving between long-range architecture, detailed engineering review, lab validation, vendor qualification, field deployment, and operational troubleshooting. Key Responsibilities Design and optimize electrical topologies and equipment strategies that reduce cost, accelerate schedules, improve efficiency, increase scalability, and maintain high reliability and maintainability. Review and develop basis-of-des

About the Role We’re hiring a Data Scientist to support Real Estate & Workplace (REW), a fast-moving global team focused on creating workplaces that help OpenAI’s people do their best work while scaling the company’s real estate and workplace operations. Our work is grounded in understanding how people use space and services, collaborate across physical and digital environments, and experience the workplace. REW’s scope spans portfolio strategy, design and construction, space planning, sustainability, workplace experience, and global operations. You’ll work comfortably across this broad, sometimes messy data landscape and build trusted relationships across the domain. The work informs high-impact decisions with immediate, visible effects—from where teams work and how space and services are allocated to which investments move forward and how workplace experiences evolve. This is a high-ownership Data Science role spanning analytical strategy and hands-on execution. Working at the forefront of AI-native analytics, you’ll help define the future of workplace operations at OpenAI rather than follow an established playbook. You’ll shape REW’s Data Science roadmap, identify where forecasting, experimentation, and optimization can drive impact, and translate business priorities into an analytical plan. You’ll own the stakeholder-facing execution layer—including owning agent-built dashboards, recurring reporting, models, and decision tools—along with analytical requirements, validation, adoption, and measurable business impact. In this role, you’ll be partnered closely with Finance, People Analytics, IT, and REW leaders. You’ll own problems end to end—from framing and prioritization through analysis, recommendation, delivery, adoption, and iteration—so the work drives measurable business outcomes. What You’ll Do Own ambiguous, high-impact problems end to end—from framing and prioritization through delivery, adoption, and iteration. Define success metrics and build measur

About the Team OpenAI is building the infrastructure foundation for the next generation of AI. The Data Center Engineering team defines the strategy, reference architectures, technical requirements, and delivery standards for the large-scale data centers that support OpenAI research, products, and infrastructure partners. As a Data Center Infrastructure Engineering Program Manager, you will help turn complex infrastructure strategy into executable programs across electrical, mechanical, controls, network, hardware, construction, commissioning, deployment, and operations workstreams. You will partner with research, hardware engineering, data center engineering, site development, supply chain, security, EHS, finance, legal, operations, and external delivery partners to bring OpenAI's infrastructure vision to life. About the Role We are looking for an Engineering Program Manager (EPM) to lead assigned infrastructure programs focused on production and non-production network integration, controls coordination, and the design and deployment of data hall or whitespace facilities. The EPM will support functional Directly Responsible Individuals (DRIs) across network, controls, structural, electrical, and mechanical disciplines. Key responsibilities include coordinating assigned workstreams and program controls, maintaining risks and interfaces, and supporting readiness within the network and data hall deployment track. The ideal candidate thrives on bringing structure to complex environments characterized by ambiguous technical requirements, large partner ecosystems, tight deadlines, and high operational stakes. This individual must be adept at keeping teams aligned on decisions, risks, dependencies, schedules, and readiness criteria, and escalating gaps or decision points when needed. Candidates should have a proven track record of managing technically challenging engineering programs across major lifecycle phases, including design, validation, procurement, construction, c

What you’ll do Execute weekly system-level exploratory testing across the scanner and supporting software; log and triage issues with clear reproduction steps. Work with engineering to debug root cause and validate fixes. Help maintain the DHF and traceability between user needs, design requirements, tests, and results. Own practical test execution logistics (fixtures, test data, environments, calibration artifacts) and keep things repeatable. Help build the continuous testing strategy: automated tests where feasible, plus structured manual and system tests. Support V&V activities, including coordination with external partners as needed. What we’re looking for Strong hands-on testing instincts for complex electromechanical systems with substantial software. Ability to write clear bug reports and communicate risk/impact. Experience building and maintaining test plans/protocols; comfort operating lab equipment and debugging across layers. Useful experience Experience testing complex systems end-to-end (automation where it pays off, plus hands-on hardware/instrumentation). Medical device or other safety-critical environments and comfort translating risk into practical test coverage.

About the Team The IT and Security organization builds the systems, data foundations, and automation that help OpenAI operate securely and reliably at scale. We support critical domains across identity, access, infrastructure security, enterprise systems, and internal productivity. As OpenAI grows, audit readiness and control assurance increasingly depend on reliable data: accurate system inventories, access populations, change records, configuration state, exception signals, and evidence generated directly from source systems. Our goal is to move beyond manual evidence collection and build scalable data products, automated validation, and continuous control monitoring that make security and IT controls measurable, repeatable, and defensible. About the Role We are looking for an IT Controls Data Engineer to build the data infrastructure that powers audit readiness, IT controls, evidence automation, and continuous control monitoring. In this role, you will design and maintain the pipelines, datasets, models, validation logic, dashboards, and evidence exports that make IT controls measurable, repeatable, and defensible. You will work across Security, IT, Infrastructure, Engineering, Finance Risk Management, and auditors to turn complex system behavior into reliable control data products. This is a technical builder role. The ideal candidate is strong in data engineering and analytics engineering, comfortable working with enterprise and security system data, and able to explain data lineage, source-system behavior, and control logic clearly to technical and audit stakeholders. You’ll be responsible for Building reliable data pipelines, models, and datasets for IT controls, including access, identity, configuration, change, ticketing, exception, and evidence data. Creating data quality, lineage, reconciliation, and completeness checks that make control data defensible for SOX and other audit use cases. Designing automated evidence generation workflows that produce compl

About the Team OpenAI’s People team hires, engages, and retains world-class talent to safely build and deploy AGI that benefits all of humanity. The People Analytics team helps leaders make rigorous, evidence-based talent decisions and ensures that the systems supporting those decisions are valid, reliable, fair, and accountable. About the Role As a People Data Scientist focused on AI fairness and bias testing, you will help establish how OpenAI evaluates AI-assisted People systems and high-impact talent processes. You will design and conduct rigorous assessments to identify, measure, and mitigate potential bias across the lifecycle of models, agents, decision-support tools, and automated workflows. Your work will span the entire employee life-cycle, such as hiring, performance, promotion, employee development, workforce planning, etc. You will evaluate both technical systems and the broader human-AI decision processes in which they operate, examining not only model performance but also data quality, measurement validity, differential outcomes, human oversight, and unintended consequences. We’re looking for an experienced data scientist or applied researcher who can translate complex fairness questions into defensible evaluation strategies, scalable testing infrastructure, and clear recommendations for technical teams and senior leaders. This role is preferred to be based in San Francisco, CA. In this role, you will: Define and lead fairness and bias-testing strategies for AI-assisted People processes, models, agents, and decision-support systems from development through deployment and ongoing monitoring. Design rigorous algorithmic audits and validation studies, including adverse-impact analysis, subgroup and intersectional evaluation, error-rate analysis, calibration, measurement invariance, reliability, criterion-related validity, and sensitivity testing. Identify the appropriate fairness criteria for each use case, evaluate tradeoffs among competing definitions

About the Team OpenAI's data and storage infrastructure spans data platforms, online databases, and file/object storage. These systems underpin data ingestion and processing, durable persistence, indexing and retrieval, and product file experiences. As frontier models and agents evolve how they use memory, history and snapshots, the underlying architecture increasingly shapes the capabilities products can deliver—and their latency, reliability, cost and efficiency. About the Role We are looking for a technically deep TPM to independently define and lead multiple programs across data platforms, online databases and storage infrastructure. You will connect model, product and data-consumer requirements to architecture, and work with the relevant engineering teams to take new capabilities through production adoption and repeatable expansion. The design scope is exabyte-scale storage and infrastructure spanning multiple millions of CPU cores. The challenge is not simply forecasting more resources: it is making complete, workload-ready capacity repeatable, with a clear path from product requirements through architecture, deployment and validation. A data pipeline, database query, file operation or execution snapshot can affect whether a product or agent succeeds; you will connect those outcomes to the systems underneath. 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: Translate model, product and data-platform needs into precise access patterns, consistency, durability, freshness, availability and scalability requirements. Connect memory, history, retrieval and resumable work to capability and end-to-end latency. Partner with engineering to transform data and storage architecture into repeatable scale units: standardized provisioning, placement, routing, data movement and readiness checks that bring storage, compute and networking online together.

About the Team The Workload team is responsible for designing and running OpenAI’s LLM training and inference infrastructure that powers frontier models at massive scale. Our systems unify how researchers train and serve models, abstracting away the complexity of performance, parallelism, and execution across vast GPU/accelerator fleets. By providing this foundation, the Workload team ensures that researchers can focus on advancing model capabilities while we handle the scale, efficiency, and reliability required to bring those models to life. About the Role We are looking for an engineer to design and implement the dataset infrastructure that powers OpenAI’s next-generation training stack. You will be responsible for building standardized dataset interfaces, scaling pipelines across thousands of GPUs, and proactively testing performance bottlenecks. In this role, you will collaborate closely with the multimodal researchers, and other infra groups to ensure datasets are unified, efficient, and easy to consume. In this role, you will: Design and maintain standardized dataset APIs, including for multimodal (MM) data that cannot fit in memory. Build proactive testing and scale validation pipelines for dataset loading at GPU scale. Collaborate with teammates to integrate datasets seamlessly into training and inference pipelines, ensuring smooth adoption and a great user experience. Document and maintain dataset interfaces so they are discoverable, consistent, and easy for other teams to adopt. Establish safeguards and validation systems to ensure datasets remain reproducible and unchanged once standardized. Debug and resolve performance bottlenecks in distributed dataset loading (e.g., straggler systems slowing global training). Provide visualization and inspection tools to surface errors, bugs, or bottlenecks in datasets. You might thrive in this role if you: Have strong engineering fundamentals with experience in distributed systems, data pipelines, or infrastructure.

About the Team OpenAI’s mission is to ensure that artificial general intelligence benefits all of humanity. Our product teams work across research, engineering, product, data, and design to bring OpenAI’s technology to people and businesses around the world. Design plays a critical role in making powerful AI intuitive, useful, and trustworthy. We hold a high bar for quality and build experiences that earn users’ confidence, while moving quickly and learning from real-world feedback. We favor early validation and continuous refinement over waiting until every detail is perfect. About the Role We’re looking for a Product Design Leader to shape how startups, small businesses, and growing teams discover, adopt, and unlock lasting value from Codex and ChatGPT. You’ll lead design across the B2B growth journey, creating experiences that turn the potential of AI into meaningful, everyday impact for this user base. This role is a mix of team leadership and hands-on design execution. You’ll lead and develop a small team of designers, contribute directly to high-impact projects, and help set the standard for impactful, simple, user-centric experiences. You balance exceptional craft with momentum, creating a culture where the team launches, learns, and improves quickly. You already use tools such as Codex to extend what you can build. As a leader, you help designers strengthen their judgment, confidence, and influence through clear and actionable feedback. You’re also a highly effective cross-functional partner who brings people together, navigates ambiguity, and builds alignment through clear communication and collaborative problem-solving. Codex is on an extraordinary trajectory, and this role offers a rare opportunity to work across both sides of the product: the consumer experiences through which people first discover and adopt our technology, and the business experiences that help teams use it together. You’ll bring a high bar for craft, strong product judgment, and comfor

About the Team The Emerging Products team is a lean, high-output product lab group that builds products at the forefront of model capabilities. We collaborate across all teams within the company, from research and infrastructure to consumer products. The team is responsible for identifying new product opportunities, building them quickly, dogfooding them internally, and then launching the successful products to users. We use data, user research, and analytics to inform our ideas, and make decisions on what experiments are worth iterating, stopping, or scaling. About the Role We’re looking for a senior, product-minded software engineer to own ambiguous 0-to-1 work from idea through prototype, validation, and handoff. This is a full-stack role with a strong frontend and product emphasis: you will build the interfaces and supporting backend systems needed to test new experiences quickly, while making sound architectural choices that enable successful concepts to scale. This role is based in our Mission Bay office in San Francisco. In this role, you will: Build and ship high-quality, product experiments across the full stack. Turn ambiguous user needs and emerging technical capabilities into testable product concepts, using research and metrics to guide iteration. Own technical direction for 0-to-1 projects, balancing speed, reliability, and a clear path from prototype to scalable product. Partner closely with design, product, research, and engineering teams to dogfood, evaluate, launch, and transition successful experiments. You might thrive in this role if you: Have a track record of building and shipping end-to-end products in fast-moving, startup, founder-led, growth, or other high-ownership environments. Bring strong frontend engineering skills and enough backend and systems depth to make sound full-stack architectural decisions. Pair product intuition with evidence, using user research and product data to identify opportunities and make pragmatic tradeoffs. Operat

About the Team OpenAI’s People team hires, engages, and retains world-class talent to safely build and deploy AGI that benefits all of humanity. The People Analytics team helps leaders make better, evidence-based talent decisions. About the Role As a People Research Scientist, you will bring deep expertise in research design, measurement, experimentation, and applied data science to OpenAI’s most important People programs. You will design studies, evaluate people processes, and help leaders better empower employees, strengthen organizational systems, and deliver exceptional employee experiences. This is a high-ownership individual contributor role combining hands-on research, methodological leadership, and scalable people science capabilities. We’re looking for an experienced researcher who can turn ambiguous People questions into rigorous designs, validated insights, and actionable recommendations. This role is based in San Francisco, CA or Mountain View, CA, with occasional travel to our San Francisco office. What You’ll Do: Design rigorous research and evaluation strategies for recruiting, organizational health, manager effectiveness, employee experience, and talent outcomes. Apply advanced statistical modeling, machine learning, and research methods to inform program design, evaluate effectiveness, and quantify business impact. Partner with People Operations, data engineering, and people systems teams to define data requirements, improve data quality, establish documentation standards, and ensure research datasets are governed, reproducible, and privacy-preserving. Build scalable people science infrastructure, including self-service agentic tools, automated validation workflows, reusable research datasets and analytical pipelines. Develop research playbooks that establish rigorous standards for study design, measurement, validation, and documentation, enabling high-quality, repeatable, and scalable research across the organization. Communicate findings through c

About the Team OpenAI’s Hardware organization develops AI-native silicon and system-level solutions for the unique demands of advanced AI workloads. Building on efforts like Jalapeño, the team is developing future generations of AI-native silicon and tightly integrated systems to power the next generation of frontier models. By co-designing chips, systems, tools, and methodologies, the team helps deliver faster, more efficient, and production-ready hardware for OpenAI’s supercomputing platform. About the Role We're seeking a Security Engineer to join our First-Party Hardware team. In this role, you will own the end-to-end security foundation for OpenAI's first-party AI hardware systems, working across hardware security, embedded security, system security, and practical deployment at data center scale. You will partner with silicon, hardware, firmware, infrastructure, manufacturing, operations, and security teams to define and deliver system-level device trust. This includes boot integrity, device identity, provisioning, attestation, management-plane security, storage encryption, debug controls, firmware update and recovery, RMA, and decommissioning. You will be accountable for turning threat models into requirements, requirements into implementation, and implementation into validation evidence that can support launch decisions. Location: San Francisco, CA (Hybrid: 3 days/week onsite) Relocation assistance available. In this role, you will: Own security requirements, threat models, validation strategy, and launch-readiness evidence for first-party hardware platforms from early design through production deployment. Design and review secure boot, measured boot, roots of trust, platform firmware resilience, firmware signing, recovery, and anti-rollback strategies across heterogeneous devices. Own device identity, provisioning, enrollment, attestation, certificate lifecycle, and key-management requirements across manufacturing and data center bring-up. Harden management

About the Team The Core Network Engineering team owns the end-to-end networking stack that connects OpenAI’s compute infrastructure — spanning global WAN/edge connectivity, data-center networking, and high-performance host/xPU networking used for large-scale training and inference workloads. This team is responsible for ensuring networking is never the bottleneck to model training efficiency, cluster reliability, or fleet expansion. They design and operate the systems that provide predictable, high-throughput, low-latency connectivity across some of the world’s most advanced AI infrastructure. About the Role We’re looking for engineers to help build and operate the networking foundation behind OpenAI’s frontier AI systems. Depending on your background and area of focus, you may work across host networking, datacenter fabrics, or global WAN infrastructure. The problems span low-level systems software, distributed infrastructure, protocol readiness, observability, performance engineering, automation, and large-scale network operations. You’ll work on systems where microseconds of latency, tail performance, and network reliability directly impact model training efficiency and production serving performance. This role is ideal for engineers who enjoy operating close to the hardware/software boundary and solving performance-critical infrastructure problems at massive scale. In this role, you will: Design, build, and operate networking systems that support large-scale AI training and inference infrastructure Improve performance, reliability, and scalability across host networking, datacenter fabrics, and WAN systems Develop automation for provisioning, configuration management, validation, upgrades, and lifecycle management of networking infrastructure Build tooling and observability systems for network health, performance analysis, debugging, and automated remediation Optimize network performance across technologies such as RDMA, RoCE, InfiniBand, Ethernet, and high-perf

About the Team OpenAI’s Hardware organization develops AI-native silicon and system-level solutions for the unique demands of advanced AI workloads. Building on efforts like Jalapeño, the team is developing future generations of AI-native silicon and tightly integrated systems to power the next generation of frontier models. By co-designing chips, systems, tools, and methodologies, the team helps deliver faster, more efficient, and production-ready hardware for OpenAI’s supercomputing platform. About the Role We are seeking a Technical Lead to lead deployment and operations for OpenAI’s Silicon & Systems team. This person will become the Directly-Responsible Individual responsible for bringing OpenAI’s custom silicon and associated systems into data center environments, ensuring successful deployment, bring-up, validation, operational readiness, and ongoing reliability at scale. This role sits at the intersection of silicon, systems, infrastructure, data center operations, and software. You will lead a team focused on taking new hardware platforms from lab validation into production data center deployment. You will be responsible for building the operational processes, technical workflows, tooling, and cross-functional alignment required to deploy and operate custom AI hardware reliably in OpenAI’s supercomputing infrastructure. The ideal candidate is both a strong leader and a deeply technical operator. You should be comfortable staying close to the technical details of hardware bring-up, fleet deployment, debugging, system validation, data center integration, and production operations. This role requires strong execution, excellent cross-functional judgment, and the ability to drive clarity in ambiguous, fast-moving environments. In this role, you will: Lead a team responsible for deployment and operations of OpenAI’s custom silicon and systems in data center environments Own the path from hardware bring-up and validation through production deployment, operati