Machine Learning Engineer, Core Experimentation

The role

You will lead the technical direction for ML-powered experimentation and insights capabilities. You will build production systems that learn from privacy-protected product and experimentation data to support decision-making.

What you will do

• Set and execute the technical roadmap for Generative Insights and Predictive Experimentation.
• Build cross-experiment learning systems to retrieve and synthesize historical experiments.
• Develop predictive models and simulation workflows to estimate likely impact and uncertainty.
• Create high-quality datasets and feature or retrieval pipelines.
• Establish rigorous evaluation through offline benchmarks, backtests, and calibration.
• Turn models into durable product, API, and agent workflows.
• Partner with data science and product teams on experiment design and causal inference.
• Build reliable services and intuitive workflows for sophisticated ML capabilities.
• Provide technical leadership across engineering, product, data science, and research partners.

Required qualifications

• Strong hands-on experience across the ML lifecycle: dataset design, training or adaptation, evaluation, deployment, monitoring, and iteration.
• Strong software engineering fundamentals in Python.
• Strong grounding in machine learning, statistics, computer science, or a related field through formal study or equivalent practical experience.
• Understand experimentation and statistical reasoning.

Nice to have

• Led ambiguous 0-to-1 production ML products.
• Depth in one or more of LLM and retrieval systems, ranking or recommendation, forecasting or anomaly detection, causal ML or experiment analysis, or simulation.

About the company

OpenAI is an AI research and deployment company dedicated to ensuring that general-purpose artificial intelligence benefits all of humanity. The Statsig team within OpenAI builds experimentation, feature rollout, dynamic configuration, and analytics systems.