Machine Learning Engineer, Implicit Signals

OpenAI is building the classification and evaluation capabilities that help research and post-training teams understand model behavior with fast, dependable feedback. In this Machine Learning Engineer role, you will take classification systems from experimentation through deployment, then operate them in production to ensure reliable measurements over time.

Working onsite in San Francisco, CA, you will own classifiers across their lifecycle, pairing machine learning development with production engineering to improve accuracy, efficiency, and usefulness at scale.

What you’ll do

• Build, deploy, and operate production classification and evaluation systems that deliver actionable feedback to post-training and research teams.
• Optimize production inference, including model serving, batching, caching, throughput, latency, compute utilization, and cost.
• Train and optimize classifiers using supervised fine-tuning and distillation, validating quality and efficiency prior to deployment.
• Own reliability and observability for deployed classifiers over time.
• Design rigorous evaluations to assess accuracy, calibration, generalization, and potential measurement bias, and to guide production tradeoffs.
• Collaborate with software engineers to integrate models into reliable execution systems and support safe rollouts.
• Create reusable engineering tools that speed up classifier training, evaluation, deployment, and iteration.
• Work with post-training researchers and data scientists to translate needs into modeling objectives, evaluation criteria, and production requirements.

What you bring

• Experience delivering ML projects from problem formulation and experimentation through production deployment.
• Strong foundations in supervised learning, statistics, and experimental design.
• Practical experience evaluating models, diagnosing systematic errors, and working with imperfect labels.
• Strong software engineering skills with the ability to build maintainable production ML systems.
• Ability to reason about inference performance and tradeoffs between quality, latency, and compute cost.
• Comfort applying scientific judgment alongside a practical approach to shipping and operating software.
• Ability to work closely with researchers and engineers on open-ended problems.
• Fluency in using AI agents to move quickly across multiple workstreams in a fast-paced environment.

Team and role context

• The team builds classification and evaluation systems that provide post-training and research teams with fast, reliable feedback on model behavior.
• Work blends machine learning with production engineering to make measurements accurate, efficient, and scalable.
• You will partner with research teams to develop useful evaluations and run them in practical ways.
• The scope spans classifier development, rigorous evaluation, efficient production inference, and continued improvement from initial experimentation through deployment.

Compensation and location

• Location: San Francisco, CA (onsite)
• Salary: USD 381,000 - 510,000 per year
• Compensation: $381K – $510K + Offers Equity
• Note: Base pay may vary based on individualized factors and eligible bonus(es), and OpenAI may modify base pay and other compensation components over time based on performance, team or company results, or market conditions.

Benefits

• 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, multiple paid coordinated company office closures, and 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.
• Daily meals in 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.