Software Engineer, Monetization ML Infrastructure

OpenAI

San Francisco, USremote country$293k-$441k/yrPosted Jun 1, 2026
Posting intelligenceActively listed

Skills

openaiasanaml

About the role

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.

To notify OpenAI that you believe this job posting is non-compliant, please submit a report through this form https://form.asana.com/?d=57018692298241&k=5MqR40fZd7jlxVUh5J-UeA. No response will be provided to inquiries unrelated to job posting compliance.

We are committed to providing reasonable accommodations to applicants with disabilities, and requests can be made via this link https://form.asana.com/?k=bQ7w9h3iexRlicUdWRiwvg&d=57018692298241.

OpenAI Global Applicant Privacy Policy https://cdn.openai.com/policies/global-employee-and-contractor-privacy-policy.pdf

At OpenAI, we believe artificial intelligence has the potential to help people solve immense global challenges, and we want the upside of AI to be widely shared. Join us in shaping the future of technology.

Compensation

This MLOps Engineer role pays $293k-$441k/yr. Within typical range for mlops engineer roles in United States.

Questions about this role

Click "Apply with AI Applyd" above and you are done. Your resume is rewritten for this advert, the screening questions are answered, and it is submitted on OpenAI's own hiring system. No retyping your history, no fourteen tabs, no evening lost.

Compensation for MLOps Engineer roles in United States varies widely by seniority, employer size, and remote vs onsite arrangement. Check the salary range on this listing when published, or browse our MLOps Engineer hub for United States medians across recent openings.

You never touch the form - the application is filled and submitted for you on OpenAI's own hiring system. It is not marked sent when we press submit. It is marked sent when a confirmation from their system arrives at the address we apply with, and your dashboard shows which stage each application is at until then.

Twelve applicant tracking systems have a real apply path: Workday, Greenhouse, Lever, Ashby, Workable, iCIMS, Personio, Recruitee, Teamtailor, Rippling, Breezy and SmartRecruiters. Your application goes in on the employer's own hiring system, never into an aggregator queue.

Want AI Applyd to auto-apply to roles like this?

We tailor your resume per posting, fill the forms, and track replies for you.