About the role
About
the Team The Monetization team is a cross-functional group working across engineering, product, research, and design to build the foundational systems that will help OpenAI scale access to intelligence responsibly. Our mission is to develop user-first, privacy-preserving monetization products, including next-generation ads experiences that strengthen user trust, unlock economic opportunity, and support OpenAI’s long-term innovation. Monetization plays a critical role in enabling OpenAI to continue pushing the boundaries of AI capabilities while ensuring the
benefits of AGI are broadly shared. We believe monetization must be aligned with user value, uphold rigorous privacy and safety standards, and sustain a healthy ecosystem of developers and businesses. This team operates in a greenfield environment and moves quickly through prototyping, experimentation, and iterative deployment. We partner closely with Product, Design, and Research to bring research breakthroughs into real-world systems at global scale.
About
the Role We’re looking for an experienced Software Engineer to build measurement systems that connect ad interactions to meaningful advertiser outcomes while protecting user privacy. In this foundational role, you’ll design infrastructure for conversion signals, attribution, reporting, and feedback loops across OpenAI’s ads products. This role is ideal for engineers who have built large-scale ads measurement, data, experimentation, marketplace, or distributed systems and want to apply that experience in a highly ambiguous 0→1 environment. You’ll work across event collection and normalization, deduplication and matching, attribution and modeled measurement, privacy-safe aggregation, reporting, and high-quality labels for ads optimization. We are hiring engineers who can independently own complex systems, make sound technical tradeoffs, and help define what should be built. You’ll work closely with Ads Delivery, Ads ML, Product, Research, Privacy, Data Science, Design, and advertiser-facing teams to create trustworthy measurement from first principles. This role is based in San Francisco or Seattle. We offer relocation assistance to new employees. In this role, you will Design and build reliable conversion-event collection and processing systems across pixels, SDKs, server-side APIs, app events, offline uploads, and advertiser integrations. Build attribution and measurement infrastructure for observed and modeled conversions, attribution windows, deduplication, delayed events, and signal-loss mitigation. Develop privacy-preserving identity, matching, aggregation, and reporting systems that provide useful measurement under strict privacy constraints. Create data-quality systems that detect missing, duplicated, malformed, out-of-order, fraudulent, or misconfigured conversion signals. Build scalable advertiser reporting and diagnostics for conversions, cost per action, return on ad spend, funnel performance, and measurement health. Partner with Ads ML to deliver trustworthy conversion labels and feedback loops for ranking, bidding, targeting, and budget optimization. Develop experimentation and incrementally capabilities, including holdouts, lift studies, and causal measurement foundations. Define the technical strategy and roadmap for conversion measurement across OpenAI’s ads delivery stack. Operate systems with high engineering rigor through testing, observability, privacy reviews, incident response, and operational best practices. You might thrive in this role if you Have 10+ years experience building and operating large-scale distributed data or backend systems, ideally in ads measurement, attribution, analytics, marketplaces, experimentation, or adjacent domains. Understand conversion-event pipelines, attribution, deduplication, identity and matching, reporting, or downstream optimization signals. Have experience designing privacy-safe measurement systems, including aggregation, consent handling, modeled conversions, clean-room patterns, or privacy-preserving APIs. Can reason about data correctness, delayed and out-of-order events, schema evolution, reconciliation, and internal-versus-external metric discrepancies. Have built high-throughput batch or streaming systems and can make sound tradeoffs across latency, reliability, cost, and maintainability. Are comfortable partnering with ML and data science teams on labels, model inputs, experimentation, and causal measurement. Think holistically across architecture, product semantics, data quality, observability, privacy, and advertiser trust. 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, system health, measurement credibility, and long-term product direction. About OpenAI OpenAI is an AI research and deployment company dedicated to ensuring that general-purpose artificial intelligence
