OpenAI

Software Engineer, Conversion Measurement

OpenAI5 days ago
Location

San Francisco

Type

Full Time

Salary

USD 293,000 – 385,000

Level

Senior

Role

Backend Engineer

Posted

Jul 20, 2026

Full TimeSenior

The role

Summary

Join OpenAI's Monetization team as a Software Engineer specializing in Conversion Measurement to architect privacy-preserving measurement systems that connect ad interactions to advertiser outcomes at global scale. This foundational 0→1 role requires 10+ years of experience building large-scale distributed systems, ideally in ads measurement, attribution, or analytics, to design infrastructure spanning event collection, deduplication, attribution modeling, and privacy-safe reporting while partnering with product, ML, and research teams to establish trustworthy conversion measurement from first principles.

What you'll do

Design and Build Conversion Event Collection Systems: Architect reliable conversion-event collection and processing systems handling multiple integration channels including pixel-based tracking, SDKs, server-side APIs, mobile app events, offline uploads, and direct advertiser integrations. Ensure robust data ingestion across diverse sources with comprehensive error handling and validation mechanisms.
Build Attribution and Measurement Infrastructure: Develop sophisticated attribution and measurement systems for both observed and modeled conversions, including attribution window management, signal deduplication, handling of delayed events, and signal-loss mitigation techniques. Implement statistical models to enhance measurement accuracy and account for data collection limitations.
Develop Privacy-Preserving Identity and Aggregation Systems: Create privacy-safe identity matching, aggregation, and reporting infrastructure that delivers actionable measurement insights while adhering to strict privacy constraints and regulatory requirements. Implement techniques such as differential privacy, clean-room patterns, and consent-aware aggregation.
Implement Data Quality and Validation Systems: Build comprehensive data-quality monitoring systems capable of detecting missing, duplicated, malformed, out-of-order, fraudulent, and misconfigured conversion signals. Develop automated remediation and alerting mechanisms to maintain measurement integrity and identify systemic issues.
Create Scalable Advertiser Reporting and Diagnostics: Build scalable reporting infrastructure delivering actionable insights on conversions, cost per action (CPA), return on ad spend (ROAS), funnel performance, and measurement health. Design intuitive dashboards and diagnostic tools for advertisers to understand and trust their measurement data.
Partner with ML and Ads Optimization Teams: Collaborate closely with Ads ML teams to deliver high-quality conversion labels and real-time feedback loops that power ranking, bidding, targeting, and budget optimization algorithms. Establish data contracts and feedback mechanisms ensuring labels are trustworthy and directly improve advertiser outcomes.
Develop Experimentation and Incrementality Capabilities: Build foundational systems for causal measurement including holdout experiments, lift studies, and incrementality testing. Enable rigorous measurement of true advertising impact and validate that observed conversions represent genuine incremental value driven by ad exposure.
Define Technical Strategy and Roadmap: Establish technical vision and long-term roadmap for conversion measurement across OpenAI's entire ads delivery stack. Make strategic architectural decisions that balance current product needs with future scalability, privacy requirements, and measurement innovation.
Operate Systems with Engineering Rigor: Maintain systems through rigorous engineering practices including comprehensive testing strategies, production observability, privacy compliance reviews, incident response protocols, and operational best practices. Ensure measurement systems maintain high reliability, accuracy, and trustworthiness in production.

What we look for

Technical

Large-Scale Distributed Systems ArchitectureDemonstrated expertise designing and operating large-scale distributed data and backend systems handling billions of events. Proficiency with high-throughput batch and streaming data processing, making sound architectural tradeoffs between latency, reliability, cost, and maintainability.
Ads Measurement and Attribution SystemsDeep understanding of conversion-event pipelines, attribution modeling, deduplication algorithms, identity matching techniques, and downstream optimization signal generation. Experience implementing attribution windows, handling delayed events, and reconciling internal versus external metric discrepancies.
Privacy-Preserving Measurement TechniquesKnowledge of privacy-safe measurement system design including aggregation patterns, consent handling mechanisms, modeled conversions, clean-room architectures, and privacy-preserving APIs. Familiarity with differential privacy, federated learning, or encrypted computation approaches.
Data Quality and Correctness VerificationExpertise in reasoning about data correctness, schema evolution, reconciliation mechanisms, and data validation. Experience detecting and handling corrupted, delayed, out-of-order, and duplicate events within distributed systems.
Experimentation and Causal InferenceComfortable collaborating with data science and ML teams on experimental design, causal measurement foundations, lift studies, and incrementality testing. Understanding of statistical rigor and measurement validation in product environments.
Holistic Systems ThinkingAbility to reason across architecture, product semantics, data quality, observability, privacy requirements, and stakeholder trust. Experience communicating technical tradeoffs and building consensus across cross-functional teams on complex measurement decisions.

Education

Bachelor's Degree in Computer Science or Related FieldFormal education in Computer Science, Computer Engineering, Mathematics, Physics, or equivalent practical experience demonstrating strong foundational computer science knowledge.

Experience

Large-Scale Backend System Development10+ years of professional experience building, deploying, and operating large-scale distributed backend systems at technology companies. Experience designing systems processing billions of events daily with focus on reliability, performance, and operational excellence.
Ads Technology or Analytics Platform ExperienceProven track record in ads measurement, attribution, analytics platforms, marketplaces, experimentation infrastructure, or adjacent domains. Experience understanding advertiser needs and building measurement solutions that directly impact business outcomes.
0-to-1 Product Development in Ambiguous EnvironmentsTrack record independently owning complex systems from conception through production deployment. Comfort defining technical direction, making architectural tradeoffs, and driving complex initiatives across organizational boundaries in high-ambiguity scenarios.
Cross-Functional Collaboration and LeadershipExperience partnering effectively with product, design, research, data science, and privacy teams. Demonstrated ability to influence technical direction, communicate complex tradeoffs to non-technical stakeholders, and build consensus on measurement standards.

Skills

Required skills

Backend System Design and ArchitectureDesign patterns for scalable distributed systems, microservices architecture, database selection and optimization, and system reliability engineering.
Data Pipeline and ETL DevelopmentBuilding robust data ingestion, transformation, and aggregation pipelines. Experience with schema design, data validation, and handling real-world data quality challenges at scale.
SQL and Data Query OptimizationAdvanced SQL proficiency for complex queries, data analysis, and query optimization. Experience with both OLTP and OLAP systems.
Distributed Computing FrameworksHands-on experience with Apache Spark, Hadoop, Flink, or similar technologies for large-scale batch and streaming data processing.
Privacy Engineering and ComplianceUnderstanding of privacy-by-design principles, regulatory frameworks (GDPR, CCPA), differential privacy concepts, and privacy audit processes.
Observability and MonitoringExpertise designing comprehensive logging, metrics, and tracing systems. Experience with observability platforms for production system monitoring and debugging.
Event Stream ProcessingExperience processing and analyzing high-volume event streams with technologies like Kafka, Pub/Sub, or similar event-driven architectures.
Python or Java Backend DevelopmentProficiency in Python or Java for backend system implementation, data processing, and infrastructure code.

Nice to have

ClickHouse or Columnar Database ExpertiseExperience with ClickHouse, BigQuery, Snowflake, or similar columnar OLAP systems optimized for analytics and aggregation workloads.
Ads Ecosystem UnderstandingFamiliarity with ads delivery systems, bidding mechanisms, auction dynamics, and advertiser interfaces. Understanding of how measurement systems integrate with ad tech stacks.
Machine Learning Systems IntegrationExperience productionizing machine learning models, building feature pipelines, and establishing data contracts between infrastructure and ML systems.
Go or Rust Systems ProgrammingExperience with Go or Rust for performance-critical infrastructure components, particularly for high-throughput event processing systems.
Experimentation Platform DevelopmentBuilding experimentation infrastructure supporting A/B testing, multi-armed bandits, and causal inference at scale.
Public Cloud Platform ExperienceProduction experience with Google Cloud Platform (GCP), Amazon Web Services (AWS), or Microsoft Azure for deploying distributed systems.
Statistical Analysis and Causal InferenceUnderstanding of statistical methods, causal inference techniques, and ability to collaborate effectively with data scientists on measurement validation.
Ad Measurement StandardsFamiliarity with industry standards like MRC (Media Rating Council), IVT (Invalid Traffic) detection, or similar measurement compliance frameworks.

Compensation & benefits

Salary

USD 293,000 – 385,000 (annual)

Stock options

Available

Benefits

Comprehensive Health Coverage

Medical, dental, and vision insurance with competitive coverage for employees and dependents. OpenAI covers a significant portion of premiums.

Retirement Savings Plan

401(k) retirement plan with company matching contributions to support long-term financial planning.

Flexible Work Arrangements

Options for remote work flexibility and flexible scheduling to support work-life balance and productivity.

Equity and Stock Options

Meaningful equity grants allowing employees to participate in OpenAI's growth and long-term success as the company scales.

Professional Development and Learning

Educational reimbursement for conferences, courses, and certifications to support continuous technical growth and industry engagement.

Relocation Assistance

Comprehensive support for relocating employees to San Francisco or Seattle offices, including moving costs and temporary housing arrangements.

Generous Time Off Policy

Competitive paid time off, including vacation days, sick leave, and holidays to ensure adequate rest and personal time.

Mental Health and Wellness Programs

Access to mental health resources, wellness programs, and employee assistance programs supporting overall well-being.

Collaborative Culture and Mission-Driven Work

Opportunity to work on foundational AI safety and benefit, surrounded by world-class researchers and engineers in a mission-driven organization.

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OpenAI

OpenAI

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OpenAI is an American artificial intelligence research organization developing advanced AI models like GPT. Focused on ensuring AI benefits humanity, it creates tools for natural language processing and generative AI applications.

San Francisco, California, United StatesFounded 2015openai.com

Tech Stack

Languages
PythonJavaSQL
Frameworks
Apache SparkApache KafkaApache Flink
Databases
ClickHousePostgreSQLBigQuery
Tools
Prometheus and GrafanaELK Stack (Elasticsearch, Logstash, Kibana)Git and CI/CD PipelinesDocker and Kubernetes
Other
Privacy Engineering FrameworksData Serialization FormatsTesting and Validation Frameworks

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