Machine Learning Engineer, Growth
OpenAI · San Francisco
About this role
## 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. We partner 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’re 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’ll develop **budget allocation and control algorithms** that determine where to invest, how quickly to spend, and how to adjust as performance changes—making reliable decisions from noisy, delayed, and incomplete signals. You’ll combine **machine learning, optimization, and experimentation** to improve incremental growth and long-term user value within budget and efficiency constraints. You’ll own the work end-to-end: **problem formulation → prototyping → deployment → evaluation → ongoing operation**. **Location:** San Francisco, CA (hybrid: 3 days in office per week). Relocation assistance may be available for eligible employees. ## In This Role, You Will - **Build budget allocation algorithms** to distribute investment across channels, campaigns, and markets using estimates of marginal returns, accounting for diminishing returns, uncertainty, and business constraints. - **Develop spend pacing & adaptive control systems** to adjust budgets and spending rates as performance changes, balancing responsiveness with stability under delayed/noisy feedback. Include spend limits, monitoring, and safe fallback behavior. - **Model marketing response & user value** with forecasts and models of conversion, long-term value, and saturation to identify where the next dollar has the greatest impact. - **Evaluate decisions rigorously** using simulations, backtesting, and online experiments. Partner with Data Science to measure incrementality and validate that offline improvements translate into better investment outcomes. - **Own reliable production systems**: build data pipelines, training/inference workflows, and integrations that turn model outputs into budget decisions. Implement data-quality checks, monitoring, and retraining with privacy/consent considerations. - **Turn business goals into optimization problems** by partnering with Growth Marketing, Data Science, Engineering, and Finance to define objectives/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 If You Have - **5+ years** of experience as a Machine Learning Engineer, Applied Scientist, Data Scientist, or comparable quantitative engineering role (or equivalent 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**; hands-on experience with ML frameworks
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