Build and ship production classification and evaluation capabilities that translate research needs into reliable, measurable model behavior.

Build, deploy, and operate production classification and evaluation systems that deliver actionable feedback to post-training and research teams
Optimize production inference performance, 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 measure accuracy, calibration, generalization, and potential measurement bias to inform production tradeoffs
Partner with software engineers to integrate models into reliable execution systems and support safe rollouts
Create reusable engineering tools to accelerate and standardize classifier training, evaluation, deployment, and iteration
Collaborate with post-training researchers and data scientists to convert needs into modeling objectives, evaluation criteria, and production requirements

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

Build classification and evaluation systems that provide post-training and research teams fast, reliable feedback on model behavior
Combine machine learning and production engineering to deliver accurate, efficient capabilities at scale
Work closely with research teams to develop useful measurements and make them practical to run
Own classifier development, rigorous evaluation, and efficient production inference, from initial experimentation through deployment and continued improvement

Develop new classification and evaluation capabilities and bring them into production
Translate open-ended questions about model behavior into well-defined ML problems, implement methods to address them, and establish reliability
Work across traditional machine learning, model evaluation, and real-time engineering
Own classifiers through the full lifecycle: supervised fine-tuning, distillation, evaluation, deployment, and production inference
Partner with researchers to identify valuable problems and with software engineers to scale promising approaches

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, plus multiple paid coordinated office closures throughout the year for focus and recharge
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

USD 381,000 - 510,000 per year + offers equity
Base pay may vary based on individualized factors, including performance-related bonus(es) for eligible employees and the benefits listed
Role is at-will; OpenAI may modify base pay and other compensation components at any time based on individual performance, team or company results, or market conditions

Machine Learning Engineer, Implicit Signals

Job Description