# Software Engineer, Conversion Measurement

**Company:** [OpenAI](https://hotfix.jobs/companies/openai)
**Location:** San Francisco, CA
**Role:** Backend Engineering
**Salary:** $293k – $385k/yr
**Experience:** 10+ years
**Skills:** Distributed Systems, Data Pipelines, Attribution, privacy-preserving systems, aggregation, streaming systems, batch processing, Experimentation, ads measurement, ml collaboration
**Posted:** 2026-07-20

> Build foundational privacy-preserving conversion measurement, attribution, and reporting infrastructure for OpenAI's ads products in a 0→1 environment. Requires 10+ years building large-scale distributed data/backend systems, ideally in ads, marketplaces, or experimentation, with strong privacy and ML partnership experience.

## Job Description

## 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 incremental 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
- 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.
- 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.
- 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.

## Nice-to-Haves
- Experience in ads measurement, attribution, analytics, marketplaces, experimentation, or adjacent domains.
- Background building privacy-safe measurement systems.

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