Software Engineer, Monetization ML Infrastructure

About the Team The Monetization team is a new 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 help build the machine learning infrastructure that powers OpenAI’s monetization and ads systems. In this foundational role, you’ll design and develop the platform layer that enables teams to build, train, deploy, serve, monitor, and continuously improve machine learning models used across advertising and monetization products. You’ll work across the full ML lifecycle, from large-scale data pipelines and feature infrastructure to training systems, model serving, experimentation platforms, and monitoring frameworks. The systems you build will support high-throughput, low-latency advertising workloads while maintaining strict standards for reliability, privacy, security, and performance. This role sits at the intersection of machine learning systems, distributed infrastructure, and monetization, offering the opportunity to shape the core platforms that help translate model innovation into measurable business impact. In this role, you will: - Design and build the ML infrastructure that powers OpenAI’s monetization and ads systems. - Develop large-scale data pipelines that process impressions, clicks, conversions, advertiser data, marketplace signals, and other inputs used to train and improve machine learning models. - Create scalable model training platforms that support ranking, conversion prediction, quality prediction, bidding, targeting, measurement, and optimization workloads. - Develop systems that safely and reliably move models from experimentation into production environments. - Build and improve real-time inference and serving infrastructure with strict requirements for latency, throughput, reliability, and availability. - Design experimentation frameworks that enable A/B testing, holdouts, model comparisons, ramping strategies, and measurement at scale. - Improve platform performance through optimization of training efficiency, inference latency, model throughput, infrastructure reliability, and cost effectiveness. - Collaborate closely with machine learning engineers, product engineers, data scientists, and monetization teams to accelerate the development and deployment of advertising systems. You might thrive in this role if you: - Have 7+ years of professional software engineering experience building large-scale distributed systems or machine learning infrastructure. - Have experience building platforms that support machine learning workflows, including data processing, feature engineering, model training, deployment, or serving. - Have worked with high-volume data pipelines and infrastructure handling large-scale online systems. - Have experience designing reliable, low-latency systems with strong operational and observability practices. - Are comfortable working across the ML lifecycle, from data and training systems through deployment, experimentation, and monitoring. - Have experience improving infrastructure performance, scalability, efficiency, and reliability in production environments. 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. Background checks for appl

7+ years of professional software engineering experience building large-scale distributed systems or machine learning infrastructure. Experience building platforms that support machine learning workflows (data processing, feature engineering, model training, deployment, or serving). Experience with high-volume data pipelines and infrastructure handling large-scale online systems. Experience designing reliable, low-latency systems with strong operational and observability practices. Comfortable working across the ML lifecycle—from data and training systems through deployment, experimentation, and monitoring. Experience improving infrastructure performance, scalability, efficiency, and reliability in production environments.

Design and build the ML infrastructure that powers OpenAI’s monetization and ads systems. Develop large-scale data pipelines that process impressions, clicks, conversions, advertiser data, marketplace signals, and other inputs used to train and improve machine learning models. Create scalable model training platforms that support ranking, conversion prediction, quality prediction, bidding, targeting, measurement, and optimization workloads. Develop systems that safely and reliably move models from experimentation into production environments. Build and improve real-time inference and serving infrastructure with strict requirements for latency, throughput, reliability, and availability. Design experimentation frameworks that enable A/B testing, holdouts, model comparisons, ramping strategies, and measurement at scale. Improve platform performance through optimization of training efficiency, inference latency, model throughput, infrastructure reliability, and cost effectiveness. Collaborate closely with machine learning engineers, product engineers, data scientists, and monetization teams to accelerate the development and deployment of advertising systems.