Staff Machine Learning Engineer, Ads Forecasting & Scenarios

Build the machine learning capabilities that help advertisers understand what they could achieve on Reddit and make better investment decisions.

You will lead models that estimate how campaign outcomes could change with different budgets, bids, audiences, creative performance, or objectives. The work connects supply and demand forecasting, audience availability, response prediction, and marketplace simulation to help advertisers compare alternatives and recognize valuable opportunities they may be missing.

This is a hands-on technical leadership role with ownership of modeling direction, evaluation standards, and reusable production capabilities across advertiser planning and recommendations.

• Lead forecasting and scenario modeling. Translate advertiser questions into models of delivery, reach, performance, and investment tradeoffs. Set technical direction and guide execution across the team.
• Connect inventory, demand, and response. Combine forecasts of available advertising opportunities, audience activity, marketplace competition, and predicted response to estimate achievable delivery and performance.
• Estimate outcomes under alternative choices. Develop models that account for changes in budget, bid, audience, and optimization objective, including interactions among these choices, auction dynamics, and campaign constraints.
• Quantify unrealized opportunity. Incorporate creative-performance estimates from partner teams to assess how stronger creative could have improved campaign results. Translate those inputs into potential gains in delivery, efficiency, or advertiser value.
• Build models that generalize. Apply probabilistic forecasting, multitask learning, transfer learning, hierarchical modeling, or related approaches to handle sparse segments, cold start, delayed feedback, and changing conditions.
• Establish trustworthy comparisons. Evaluate numerical accuracy, calibration, uncertainty, and cohort-level performance. Define supported scenarios and identify when additional evidence or experimentation is needed.
• Close the learning loop. Compare recorded predictions with mature outcomes, diagnose systematic errors, and validate scenario estimates through experiments and observed results.
• Own production delivery and technical leadership. Partner with engineers on reliable training, serving, monitoring, and iteration. Lead designs, mentor engineers, and collaborate with Product, Data Science, Ads Delivery, Audience, and Creative teams.

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