Software Engineer, Ads Conversion Measurement

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.

Responsibilities

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 incrementality 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.

Requirements

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.

Benefits

Relocation assistance