Machine Learning Engineer, Growth
OpenAI · Applied AI
ABOUT THE TEAM 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. Our team partners closely with Product, Engineering, Data Science, Design, Finance, Lifecycle, and Growth Marketing. We move quickly, test rigorously, and hold a high bar for measurement, privacy, reliability, and user trust. ABOUT THIS ROLE: We are looking for a Machine Learning Engineer to build the models and decision systems that optimize paid marketing investment for ChatGPT and other OpenAI products. You will develop budget allocation and control algorithms that determine where to invest, how quickly to spend, and how to adjust as performance changes. The challenge is to make reliable decisions from noisy, delayed, and incomplete signals. You will combine machine learning, optimization, and experimentation to improve incremental growth and long-term user value within budget and efficiency constraints. You will own this work from problem formulation and prototyping through deployment, evaluation, and ongoing operation. This role is based in San Francisco, CA. We use a hybrid work model of three days in the office per week and offer relocation assistance to eligible employees. IN THIS ROLE, YOU WILL: - Build budget allocation algorithms. Decide how to distribute investment across channels, campaigns, and markets using estimates of marginal returns, while accounting for diminishing returns, uncertainty, and business constraints. - Develop spend pacing and adaptive control systems. Adjust budgets and spending rates as performance changes, balancing responsiveness with stability when feedback is delayed or noisy. Build in spend limits, monitoring, and safe fallback behavior. - Model marketing response and user value. Develop forecasts and models of conversion, long-term value, and saturation that help determine where the next dollar can have the greatest impact. - Evaluate decisions rigorously. Use simulations, backtesting, and online experiments to test allocation and control policies. Partner with Data Science to measure incrementality and validate whether offline improvements translate into better investment outcomes. - Own reliable production systems. Build data pipelines, training and inference workflows, and integrations that turn model outputs into budget decisions. Establish data-quality checks, monitoring, and retraining with thoughtful treatment of privacy and consent. - Turn business goals into optimization problems. Partner with Growth Marketing, Data Science, Engineering, and Finance to define objectives and constraints, communicate tradeoffs, and use evidence to improve investment decisions. - Help build a diverse and inclusive culture where people feel welcome, challenge assumptions, and collaborate with candor and respect. You might thrive in this role if you: - 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