23 hours ago
Base Salary
$381k - $423k/yr
Responsibilities
- Build budget allocation algorithms that distribute investment across channels, campaigns, and markets while accounting for marginal returns, diminishing returns, uncertainty, and business constraints.
- Develop spend-pacing and adaptive-control systems with monitoring, spend limits, and safe fallback behavior.
- Build models and forecasts for conversion, long-term user value, marketing response, and saturation.
- Evaluate allocation and control policies using simulations, backtesting, and online experiments, and partner with Data Science on incrementality measurement.
- Own production data pipelines, training and inference workflows, integrations, data-quality checks, monitoring, and retraining.
- Translate growth and business goals into optimization objectives and constraints while communicating tradeoffs with cross-functional partners.
- Support a diverse, inclusive, candid, and respectful team culture.
Requirements
- 5+ years of experience as a Machine Learning Engineer, Applied Scientist, Data Scientist, comparable quantitative engineer, or 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 with proficiency in Python and SQL and hands-on experience with machine learning frameworks and large-scale data systems.
- Strong foundations in machine learning and statistics, including experience applying optimization or control methods to decisions under uncertainty.
- Experience evaluating models or algorithms with noisy, delayed, or incomplete data while balancing model quality, causal impact, and operational reliability.
- Ability to turn ambiguous goals into focused experiments, interpret mixed results, and communicate decisions to technical and non-technical partners.
- Commitment to deploying AI responsibly.
Benefits
- Hybrid work model with three days per week in the San Francisco office.
- Relocation assistance is available to eligible employees.
Categories
Growth EngineeringML Engineering
About OpenAI
OpenAI builds and deploys large-scale AI models and tools—including ChatGPT, GPT-4–class models, DALL·E, and Whisper—sold via APIs and enterprise subscriptions to developers and businesses. It monetizes through usage-based API pricing and ChatGPT Plus/Team/Enterprise, and also reaches customers via Microsoft’s Azure OpenAI Service. Founded in 2015 and headquartered in San Francisco, it operates as a private partnership.