Job description

ABOUT THE TEAM The Monetization team is a new cross-functional group spanning engineering, product, research, and design. We are building the foundational systems that will help OpenAI scale access to intelligence responsibly. Our mission is to create user-first, privacy-preserving monetization products—including next-generation ads experiences—that strengthen trust, expand economic opportunity, and support OpenAI’s long-term innovation. We believe monetization should deliver clear user value, uphold rigorous privacy and safety standards, and sustain a healthy ecosystem for developers and businesses. The team operates in a greenfield environment, moving quickly through prototyping, experimentation, and iterative deployment. We partner closely across Product, Design, and Research to bring new capabilities into real-world systems at global scale. ABOUT THE ROLE We’re looking for an experienced Software Engineer to lead the design and development of the systems that turn advertiser feeds (eg. retail feeds, travel feeds etc.) into relevant, reliably delivered ads. In this foundational role, you will build across the full ads lifecycle—from ingesting and organizing retailer or travel catalogs into efficient retrieval systems, supporting ranking models, and serving ads at scale. This role is well suited to an engineer with direct experience in product ads retrieval, ranking, and delivery, along with a strong understanding of merchant feeds and shopping systems such as Google Shopping, YouTube product ads, or Amazon. We also welcome engineers with Senior Staff-level or greater scope who have deep ownership of large-scale catalogs, search and indexing, ads serving, or closely related distributed systems. You will independently own complex systems, make sound technical tradeoffs, and help define the architecture and roadmap for Shopping/Travel/Feed Ads. You will collaborate closely with Ads ML, Ads Delivery, Product, Design, and Research to connect accurate, fresh advertiser feed information with useful ads experiences in ChatGPT. This role is based in San Francisco and Mountain View. We offer relocation assistance to new employees. IN THIS ROLE, YOU WILL - Design and build pipelines that ingest, validate, normalize, and refresh merchant product feeds, keeping catalog information accurate and available for serving. - Develop scalable catalog storage and indexing systems that make large inventories searchable and reflect changes in product availability, pricing, and attributes. - Build search and retrieval systems that efficiently identify relevant, eligible products across merchant catalogs. - Partner with Ads ML to connect product retrieval with ranking models through dependable interfaces, features, and serving infrastructure. - Engineer low-latency, high-throughput ads delivery systems with strong reliability and correctness guarantees. - Design real-time event pipelines and distributed storage that support catalog freshness, serving decisions, and downstream feedback. - Build observability and quality controls to detect feed errors, stale inventory, indexing delays, and serving issues. - Define technical direction and drive complex projects from early prototypes through production deployment and operation. - Operate systems with high engineering rigor through testing, monitoring, privacy and security reviews, and strong operational practices. YOU MIGHT THRIVE IN THIS ROLE IF YOU - Have extensive experience building and operating large-scale distributed systems, with direct ownership of product ads retrieval, ranking infrastructure, or delivery. - Understand merchant feeds and shopping systems, including how catalog quality, product availability, and freshness affect relevance and ads performance. - Have built systems for shopping or product ads at platforms such as Google Shopping, YouTube, Amazon, or a comparable environment. - Alternatively, have operated at Senior Staff-level scope or above, leading the architecture and delivery of large-scale catalogs, search and indexing platforms, ads serving, or related backend systems. - Have built low-latency, high-throughput services and data pipelines with demanding reliability and correctness requirements. - Can reason across ingestion, storage, indexing, retrieval, ranking, and serving, and make thoughtful tradeoffs between freshness, relevance, latency, and cost. - Are comfortable partnering with machine-learning teams to bring ranking models and signals into dependable production systems. - Can define technical direction in an ambiguous 0→1 environment and independently drive complex work across teams. - Communicate clearly and make technical decisions grounded in user value, merchant experience, and long-term system health. About OpenAI OpenAI is an AI research and deployment company dedicated to ensuring that general-purpose artificial intelligence benefits all of humanity. We push the boundaries of the capabilities of AI systems and seek to safely deploy them to the world through our products. AI is an extremely powerful tool that must be created with safety and human needs at its core, and to achieve our mission, we must encompass and value the many different perspectives, voices, and experiences that form the full spectrum of humanity. We are an equal opportunity employer, and we do not discriminate on the basis of race, religion, color, national origin, sex, sexual orientation, age, veteran status, disability, genetic information, or other applicable legally protected characteristic. For additional information, please see OpenAI’s Affirmative Action and Equal Employment Opportunity Policy Statement cdn.openai.com. Background checks for applicants will be administered in accordance with applicable law, and qualified applicants with arrest or conviction records will be considered for employment consistent with those laws, including the San Francisco Fair Chance Ordinance, the Los Angeles County Fair Chance Ordinance for Employers, and the California Fair Chance Act, for US-based candidates. For unincorporated Los Angeles County workers: we reasonably believe that criminal history may have a direct, adverse and negative relationship with the following job duties, potentially resulting in the withdrawal of a conditional offer of employment: protect computer hardware entrusted to you from theft, loss or damage; return all computer hardware in your possession (including the data contained therein) upon termination of employment or end of assignment; and maintain the confidentiality of proprietary, confidential, and non-public information. In addition, job duties require access to secure and protected information technology systems and related data security obligations. To notify OpenAI that you believe this job posting is non-compliant, please submit a report through this form form.asana.com. No response will be provided to inquiries unrelated to job posting compliance. We are committed to providing reasonable accommodations to applicants with disabilities, and requests can be made via this link form.asana.com. OpenAI Global Applicant Privacy Policy cdn.openai.com At OpenAI, we believe artificial intelligence has the potential to help people solve immense global challenges, and we want the upside of AI to be widely shared. Join us in shaping the future of technology.

Requirements

Must-haves

The Monetization team is a new cross-functional group spanning engineering, product, research, and design. We are building the foundational systems that will help OpenAI scale access to intelligence responsibly.
Design and build pipelines that ingest, validate, normalize, and refresh merchant product feeds, keeping catalog information accurate and available for serving.
Develop scalable catalog storage and indexing systems that make large inventories searchable and reflect changes in product availability, pricing, and attributes.
Build search and retrieval systems that efficiently identify relevant, eligible products across merchant catalogs.
Partner with Ads ML to connect product retrieval with ranking models through dependable interfaces, features, and serving infrastructure.
Engineer low-latency, high-throughput ads delivery systems with strong reliability and correctness guarantees.
Design real-time event pipelines and distributed storage that support catalog freshness, serving decisions, and downstream feedback.
Build observability and quality controls to detect feed errors, stale inventory, indexing delays, and serving issues.
Define technical direction and drive complex projects from early prototypes through production deployment and operation.
Operate systems with high engineering rigor through testing, monitoring, privacy and security reviews, and strong operational practices.
YOU MIGHT THRIVE IN THIS ROLE IF YOU
Have extensive experience building and operating large-scale distributed systems, with direct ownership of product ads retrieval, ranking infrastructure, or delivery.
Understand merchant feeds and shopping systems, including how catalog quality, product availability, and freshness affect relevance and ads performance.
Have built systems for shopping or product ads at platforms such as Google Shopping, YouTube, Amazon, or a comparable environment.
Alternatively, have operated at Senior Staff-level scope or above, leading the architecture and delivery of large-scale catalogs, search and indexing platforms, ads serving, or related backend systems.
Have built low-latency, high-throughput services and data pipelines with demanding reliability and correctness requirements.
Can reason across ingestion, storage, indexing, retrieval, ranking, and serving, and make thoughtful tradeoffs between freshness, relevance, latency, and cost.
Are comfortable partnering with machine-learning teams to bring ranking models and signals into dependable production systems.
Can define technical direction in an ambiguous 0→1 environment and independently drive complex work across teams.
Communicate clearly and make technical decisions grounded in user value, merchant experience, and long-term system health.

Nice-to-haves

None flagged.

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

mlai

Platform: ashby
Effort: quick