Software Engineer, Monetization ML Infrastructure

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.

Responsibilities

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

Requirements

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