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OpenAI's Monetization team is a new cross-functional group building foundational systems to scale access to intelligence responsibly. The team develops 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.
As a Machine Learning Engineer in the Monetization Group, you will work with leading AI researchers and engineers to deploy state-of-the-art models in production environments. You'll translate research breakthroughs into real-world systems at global scale, partnering closely with Product, Design, and Research teams.
Key responsibilities:
- Design and deploy advanced machine learning models that solve real-world problems, bringing OpenAI's research from concept to implementation
- Work closely with researchers, software engineers, and product managers to understand complex business challenges and deliver AI-powered solutions
- Implement scalable data pipelines, optimize models for performance and accuracy, and ensure production readiness
- Engage with the latest developments in machine learning and AI; participate in code reviews and maintain high-quality engineering practices
- Monitor and maintain deployed models to ensure they continue delivering value
- Own problems end-to-end, moving fast in a greenfield environment with rapid prototyping and iterative deployment
The team operates in a greenfield environment and moves quickly through experimentation and real-world deployment. You'll have direct impact on how AI benefits individuals, businesses, and society.
REQUIREMENTS:
- Master's or PhD degree in Computer Science, Machine Learning, Data Science, or related field
- Demonstrated experience in deep learning and transformer models
- Proficiency in PyTorch or TensorFlow
- Strong foundation in data structures, algorithms, and software engineering principles
- Experience with search relevance, ads ranking, or LLMs (preferred)
- Familiarity with methods of training and fine-tuning large language models (distillation, supervised fine-tuning, policy optimization)
- Excellent problem-solving and analytical skills with proactive approach to challenges
- Ability to work collaboratively with cross-functional teams
- Ability to move fast in loosely-defined environments with competing priorities
- Willingness to own problems end-to-end and acquire missing knowledge as needed
