Machine Learning Engineer — Implicit Signals & Evaluation

OpenAI; San Francisco (CA); Presencial; USD 381.000 - 510.000; Jornada completa

Descripción de la vacante

OpenAI is seeking a machine learning engineer to develop new classification and evaluation capabilities and bring them into production. You’ll turn open-ended questions about model behavior into well-defined ML problems and build methods that address them, with a focus on reliability and scalability.

You’ll own classifiers throughout their lifecycle—supervised fine-tuning, distillation, evaluation, deployment, and production inference—working closely with researchers and software engineers to

Formación

• 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.

Responsabilidades

• 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.

Conocimientos

ML project lifecycle

Supervised learning

Statistics

Experimental design

Software engineering

Inference performance

Scientific judgment

Collaboration with researchers

Model distillation

Herramientas

Python

ML frameworks

Descripción del empleo

OpenAI is seeking a machine learning engineer to develop new classification and evaluation capabilities and bring them into production. You’ll turn open-ended questions about model behavior into well-defined ML problems and build methods that address them, with a focus on reliability and scalability.

You’ll own classifiers throughout their lifecycle—supervised fine-tuning, distillation, evaluation, deployment, and production inference—working closely with researchers and software engineers to