Software Engineer, Monetization ML Infrastructure

OpenAI OpenAI · AI Frontier · San Francisco, CA · Applied AI

Software Engineer to build ML infrastructure for OpenAI's monetization and ads systems. This role involves designing and developing the platform layer for ML models across the full lifecycle, including data pipelines, training systems, model serving, experimentation, and monitoring, with a focus on high-throughput, low-latency advertising workloads.

What you'd actually do

  1. Design and build the ML infrastructure that powers OpenAI’s monetization and ads systems.
  2. 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.
  3. Create scalable model training platforms that support ranking, conversion prediction, quality prediction, bidding, targeting, measurement, and optimization workloads.
  4. Build and improve real-time inference and serving infrastructure with strict requirements for latency, throughput, reliability, and availability.
  5. Design experimentation frameworks that enable A/B testing, holdouts, model comparisons, ramping strategies, and measurement at scale.

Skills

Required

  • Software engineering
  • large-scale distributed systems
  • machine learning infrastructure
  • ML platforms
  • data processing
  • feature engineering
  • model training
  • model deployment
  • model serving
  • high-volume data pipelines
  • large-scale online systems
  • low-latency systems
  • operational practices
  • observability practices
  • ML lifecycle

Nice to have

  • monetization
  • advertising systems
  • privacy-preserving products
  • privacy
  • security
  • performance optimization
  • cost effectiveness

What the JD emphasized

  • 7+ years of professional software engineering experience building large-scale distributed systems or machine learning infrastructure
  • high-volume data pipelines and infrastructure handling large-scale online systems
  • low-latency systems

Other signals

  • ML infrastructure
  • full ML lifecycle
  • high-throughput, low-latency advertising workloads
  • scale