Business Operations Manager

OpenAI•San Francisco, CA

54d•Hybrid

About The Position

About the Team The Data Acquisition team within Foundations at OpenAI is responsible for all aspects of data collection to support model training. We build and operate large-scale crawling infrastructure, source and structure external datasets, and work with research, product, and partnerships to ensure OpenAI has the data needed to build and deploy frontier AI systems. We are a small, high-performing team that thrives in ambiguity. We combine technical fluency, operational rigor, and strategic thinking to move quickly on mission-critical initiatives. Team members bring structure to complex problems, align stakeholders, and drive execution across the organization. About the Role We are hiring a Business Operations Manager to lead data operations and acquisition programs. In this role, you will partner closely with research, product, and business teams to identify data needs, source and evaluate data opportunities, and ensure that high-quality data is efficiently delivered into our training pipelines. You’ll develop a deep understanding of our models, product goals, and the broader data ecosystem. You will work with researchers and engineers to clarify data requirements, design and manage acquisition workflows, engage external partners, and establish processes that improve our ability to create, acquire, and manage data at scale. This role requires a blend of strategic thinking and hands-on execution. The ideal candidate can navigate ambiguity, break down complex problems, manage cross-functional stakeholders, and drive alignment across technical and business teams. You’ll help the organization anticipate data needs, uncover new opportunities, build internal capability, and operationalize solutions that meaningfully accelerate research progress This role is based in our San Francisco HQ or New York office. We operate on a hybrid work model with three days in the office per week and offer relocation assistance.

Requirements

Have 8+ years of experience in business operations, strategy, venture capital, private equity, consulting, or operations management with at least 2 years operating within a company. We will also consider high-trajectory candidates with fewer years of experience but at top companies with a clear track record of excellence.
Have experience taking end-to-end ownership of large, ambiguous problems, and breaking them down into clear, actionable plans.
Have direct experience engaging with executives and senior leaders to influence and drive strategic decisions.
Excel under pressure, bringing a high motor and exceptional adaptability and flexibility in a fast-paced, dynamic environment.
Are highly analytical – sophisticated in your understanding and approach to analyses; ability to ask and answer the most important questions, and present results clearly to leaders to influence decision making.
Are comfortable operating at all altitudes – discussing strategy and vision with executives, and troubleshooting operations with individual contributors and external partners.
Have excellent communication and cross-functional collaboration skills.
Are passionate about technology and artificial intelligence, and interested in its impact on business and society.

Nice To Haves

Technical background with a computer science degree or work in engineering/product
Experience working in AI.
Experience working in data science

Responsibilities

Work closely with research, product, and partnerships teams to understand data needs and define acquisition priorities aligned with model and product goals.
Translate research objectives into clear technical requirements and operational plans, ensuring data workflows, timelines, and quality standards are well understood and executed.
Lead vendor and partner engagements for data sourcing—including evaluating suppliers, negotiating agreements, and managing delivery.
Establish and maintain metrics for data quality, throughput, spend efficiency, and overall program performance, ensuring visibility to leadership and cross-functional partners.
Identify gaps in our data ecosystem and proactively drive initiatives to strengthen our data acquisition and creation capabilities over time.

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