OpenAI is seeking a machine learning engineer in San Francisco to build and operationalize classification and evaluation capabilities. The role focuses on taking classifiers from training and measurement through deployment and reliable production inference, with an emphasis on rigor, observability, and dependable outcomes over time.
Key Responsibilities
- Build, deploy, and operate production classification and evaluation systems that deliver useful 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 before deployment.
- Own the reliability and observability of deployed classifiers over time.
- Design rigorous evaluations that assess accuracy, calibration, generalization, and potential measurement bias to support production tradeoffs.
- Partner with software engineers to integrate models into reliable execution systems and support safe rollouts.
- Build reusable engineering tools to make classifier training, evaluation, deployment, and iteration faster and more repeatable.
- Work with post-training researchers and data scientists to translate needs into modeling objectives, evaluation criteria, and production requirements.
Required Qualifications
- 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 and 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 careful scientific judgment with a practical approach to shipping and operating software.
- Comfort collaborating 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.
Preferred Background
- Experience reasoning about inference performance and the tradeoffs between quality, latency, and compute cost.
- Experience with model distillation, representation learning, calibration, or efficient inference (a plus).
Team Overview
The team develops classification and evaluation systems that provide fast, reliable feedback to post-training and research teams. They combine machine learning and production engineering to ensure capabilities are accurate, efficient, and useful at scale, working closely with research teams to develop measurements that are practical to run. Work spans classifier development, rigorous evaluation, efficient production inference, and ownership from initial experimentation through deployment and continued improvement.
Role Focus
This role develops new classification and evaluation capabilities and brings them into production. You will turn open-ended questions about model behavior into well-defined ML problems, build methods to address them, and establish how reliably those methods work. The position combines traditional machine learning, model evaluation, and real-time engineering, with ownership across the full classifier lifecycle, including supervised fine-tuning, distillation, evaluation, deployment, and production inference.
Compensation
USD 381,000 - 510,000 per year. Base pay offered may vary based on multiple individualized factors. The total compensation may include offers of equity. The role is at-will, and OpenAI reserves the right to modify base pay and other compensation components at any time based on individual 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, plus multiple paid coordinated company 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 to support professional growth.
- 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.
