Software Engineer, Monetization ML Infrastructure

Compensation

The base pay offered may vary depending on multiple individualized factors, including market location, job-related knowledge, skills, and experience. If the role is non-exempt, overtime pay will be provided consistent with applicable laws. In addition to the salary range listed above, total compensation also includes generous equity, performance-related bonus(es) for eligible employees, and the following benefits.

• Medical, dental, and vision insurance for you and your family, with employer contributions to Health Savings Accounts

• Pre-tax accounts for Health FSA, Dependent Care FSA, and commuter expenses (parking and transit)

• 401(k) retirement plan with employer match

• Paid parental leave (up to 24 weeks for birth parents and 20 weeks for non-birthing parents), plus paid medical and caregiver leave (up to 8 weeks)

• Paid time off: flexible PTO for exempt employees and up to 15 days annually for non-exempt employees

• 13+ paid company holidays, and multiple paid coordinated company office closures throughout the year for focus and recharge, plus paid sick or safe time (1 hour per 30 hours worked, or more, as required by applicable state or local law)

• Mental health and wellness support

• Employer-paid basic life and disability coverage

• Annual learning and development stipend to fuel your professional growth

• Daily meals in our offices, and meal delivery credits as eligible

• Relocation support for eligible employees

• Additional taxable fringe benefits, such as charitable donation matching and wellness stipends, may also be provided.

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.

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.

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.