Software Engineer, Monetization ML Infrastructure
$347k - $445k • Remote • San Francisco • FullTime
Posted 4mo ago
Job Location
San Francisco
Tech Stack
Remote Work Policy
Fully remote
Employment Type
FullTime
Categories
Machine Learning Engineer
About the job
We are seeking an experienced Software Engineer to join our new Monetization team, focused on building the foundational machine learning infrastructure for OpenAI's monetization and ads systems. This role involves designing and developing a platform that supports the entire ML lifecycle, from data pipelines and feature infrastructure to training, deployment, serving, experimentation, and monitoring. You will work on systems that handle high-throughput, low-latency advertising workloads while adhering to strict standards for reliability, privacy, security, and performance. This is a greenfield opportunity to shape core platforms that translate AI innovation into measurable business impact.
Responsibilities
- Design and build ML infrastructure for monetization and ads systems.
- Develop large-scale data pipelines for various advertising inputs.
- Create scalable model training platforms for diverse ML workloads.
- Develop systems for safely moving models from experimentation to production.
- Build and improve real-time inference and serving infrastructure with strict latency and reliability requirements.
- Design experimentation frameworks for large-scale A/B testing and model evaluation.
- Improve platform performance, scalability, efficiency, and reliability.
- Collaborate with ML engineers, product engineers, data scientists, and monetization teams.
Requirements
- 7+ years of professional software engineering experience building large-scale distributed systems or ML infrastructure.
- Experience building platforms for ML workflows (data processing, feature engineering, training, deployment, serving).
- Experience with high-volume data pipelines and infrastructure for large-scale online systems.
- Experience designing reliable, low-latency systems with strong operational and observability practices.
- Comfortable working across the full ML lifecycle.
- Experience improving infrastructure performance, scalability, efficiency, and reliability in production.