Machine Learning Engineer, Growth

Role Overview

The Growth team drives user and revenue growth across ChatGPT’s consumer and business segments and other OpenAI products worldwide. We work across the full funnel—from awareness and acquisition through activation, retention, and expansion—using experimentation, data science, machine learning, and product engineering to help more people discover and realize durable value from OpenAI’s products.

What You Will Do

Build budget allocation algorithms, develop spend pacing and adaptive control systems, model marketing response and user value, evaluate decisions rigorously, own reliable production systems, and turn business goals into optimization problems.

Why It Might Be a Fit

5+ years of experience as a Machine Learning Engineer, Applied Scientist, Data Scientist, or in a comparable quantitative engineering role, or an equivalent record of impact, strong software engineering skills, proficiency in Python and SQL, and hands-on experience with ML frameworks and large-scale data systems, strong foundations in machine learning and statistics, and the ability to turn ambiguous goals into focused experiments, interpret mixed results, and communicate clear decisions with technical and non-technical partners.

Requirements

• 5+ years of experience as a Machine Learning Engineer, Applied Scientist, Data Scientist, or in a comparable quantitative engineering role, or an equivalent record of impact
• Experience building and operating production ML or quantitative decision systems, from problem formulation and data pipelines through deployment, experimentation, and monitoring
• Strong software engineering skills, proficiency in Python and SQL, and hands-on experience with ML frameworks and large-scale data systems
• Strong foundations in machine learning and statistics, with experience applying optimization or control methods to decisions under uncertainty
• Experience evaluating models or algorithms with noisy, delayed, or incomplete data, and the judgment to balance model quality, causal impact, and operational reliability
• The ability to turn ambiguous goals into focused experiments, interpret mixed results, and communicate clear decisions with technical and non-technical partners

Benefits

• Relocation assistance
• Dental insurance
• Vision insurance
• Health insurance
• Paid time off
• Retirement plan
• Learning budget
• Parental leave
• Wellness program
• Visa sponsorship

Tagged as: Python, SQL