Principal Machine Learning Engineer

What you'll do

• Define and build a company-wide causal value metric to measure the incremental impact of product and marketplace actions.
• Establish the technical roadmap, architecture, and operating model for the Causal ML pod.
• Architect reusable systems for treatment effect estimation, counterfactual evaluation, and long-term outcome forecasting.
• Integrate randomized experiments, quasi-experiments, and observational data into a unified decision-making framework.
• Develop causal layers for production applications including search, ranking, promotions, and demand shaping.
• Set standards for the validation, monitoring, and governance of causal estimates in production environments.
• Translate complex causal evidence into actionable insights for senior leadership across product, engineering, and finance teams.
• Mentor and develop senior engineers and scientists through technical direction and design reviews.

What we're looking for

• Must have 10+ years of experience in causal inference, econometrics, experimentation, or causal machine learning.
• Experience leading the design and productionization of causal models, measurement platforms, or large-scale decision engines.
• Proven ability to set technical direction beyond a single team.
• Deep judgment regarding randomized experiments, observational methods, surrogate endpoints, and model-based decisioning.
• Fluency with methods including doubly robust estimation, double machine learning, instrumental variables, difference-in-differences, synthetic controls, and off-policy evaluation.
• Strong ML engineering and systems skills to manage data contracts, modeling pipelines, and production monitoring.
• Ability to influence executives and cross-functional partners through clear problem framing and technical judgment.
• Demonstrated ability to mentor senior engineers and scientists in causal reasoning and engineering craft.