Staff Machine Learning Engineer, Ads Forecasting & Scenarios

What you'll do

• Lead forecasting and scenario modeling to translate advertiser questions into models of delivery, reach, and performance.
• Combine forecasts of available advertising opportunities, audience activity, and marketplace competition to estimate achievable performance.
• Develop models to estimate outcomes under alternative choices like budget, bid, audience, and optimization objectives.
• Incorporate creative-performance estimates to quantify potential gains in delivery, efficiency, and advertiser value.
• Apply probabilistic forecasting, multitask learning, and hierarchical modeling to handle sparse segments and cold starts.
• Establish trustworthy comparisons by evaluating numerical accuracy, calibration, uncertainty, and cohort-level performance.
• Compare recorded predictions with mature outcomes to diagnose systematic errors and validate scenario estimates.
• Own production delivery by partnering with engineers on reliable training, serving, monitoring, and iteration.

What we're looking for

• Proven track record of developing and shipping predictive ML systems in advertising, marketplaces, recommendations, search, or demand forecasting.
• Strong foundations in statistical learning, probability, and optimization with experience modeling large-scale behavioral or transactional data.
• Depth in probabilistic or hierarchical forecasting, calibrated conversion/value prediction, multitask or transfer learning, or response modeling.
• Experience addressing sparse observations, cold start, seasonality, distribution shift, selection bias, or partially observed labels.
• Strong programming and software engineering skills, including experience building and operating production ML systems.
• Demonstrated ability to lead ambiguous technical initiatives, make practical tradeoffs, and develop other engineers.
• Complementary depth in demand/supply forecasting, auction and investment modeling, scenario evaluation, or reach and audience estimation (preferred).
• Complementary depth in mixture-of-experts, distributional or quantile prediction, temporal sequence models, or state-space models (preferred).