Production ML Engineer - Classifiers & Evaluation

FDE Times; San Francisco (CA); On-site; USD 180,000 - 250,000; Full time

Job summary

OpenAI is seeking a machine learning engineer to develop and productionize new classification and evaluation capabilities. You will turn open-ended research questions about model behavior into well-defined ML problems, build methods, and ensure their reliability at scale.

You will own classifiers across their lifecycle—from supervised fine-tuning and distillation to evaluation, deployment, and production inference—working closely with researchers and software engineers to ship robust, scalable

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.
• The ability to combine careful 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 quickly across multiple workstreams in a fast-paced environment.
• Experience with model distillation, representation learning, calibration, or efficient inference is a plus.

Responsibilities

• Build, deploy, and operate production classification and evaluation systems that provide 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, diagnose production issues, and improve their performance over time.
• Design rigorous evaluations that assess accuracy, calibration, generalization, and potential measurement bias while guiding production tradeoffs.
• Partner with software engineers to integrate models into reliable execution systems and support safe rollouts.
• Build reusable engineering tools that make classifier training, evaluation, deployment, and iteration faster and more repeatable.
• Work with post-training researchers and data scientists to translate their needs into modeling objectives, evaluation criteria, and production requirements.

Skills

Experience taking ML projects from

Problem formulation

Experimentation

Production deployment

Foundations in supervised learning

Statistics

Experimental design

Evaluation of models

Handling imperfect labels

Software engineering for production ML

Inference performance reasoning

Quality-latency-cost tradeoffs

Scientific judgment with shipping

Collaboration with researchers & Engs

Using AI agents across workstreams

Model distillation / calibration / eff

Job description

OpenAI is seeking a machine learning engineer to develop and productionize new classification and evaluation capabilities. You will turn open-ended research questions about model behavior into well-defined ML problems, build methods, and ensure their reliability at scale.

You will own classifiers across their lifecycle—from supervised fine-tuning and distillation to evaluation, deployment, and production inference—working closely with researchers and software engineers to ship robust, scalable