Software Engineer, Monetization ML Infrastructure

OpenAI

Software Engineer developing machine learning infrastructure for monetization systems at OpenAI. Focus on scalable platforms and collaborative cross-functional teamwork.

Posted 6/2/2026full-timeSan Francisco • California • 🇺🇸 United StatesSeniorLead💰 $293,000 - $441,000 per yearWebsite

Tech Stack

Tools & technologies

Distributed Systems

About the role

Key responsibilities & impact

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.

Requirements

What you’ll need

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.

Benefits

Comp & perks

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.

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills & Tools

machine learning infrastructuredata pipelinesmodel trainingA/B testingfeature engineeringdeploymentreal-time inferenceoptimizationscalabilitylow-latency systems

Soft Skills

collaborationcommunicationproblem-solvingoperational practicesobservability