Staff Machine Learning Engineer, Ads Forecasting & Scenarios
New
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Reddit, Inc.Advertising technology
Remote - United StatesFull-TimeStaff
SalaryThe base salary range for this position is: $230,000 — $322,000 USD
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Job Details
- Required Skills
- Machine Learning
Requirements
- Developed and shipped predictive machine-learning systems in advertising, marketplaces, recommendations, search, demand forecasting, or similarly complex domains.
- Strong foundations in statistical learning, probability, and optimization.
- Experience modeling large-scale behavioral or transactional data.
- Depth in probabilistic or hierarchical forecasting, calibrated conversion or value prediction, multitask or transfer learning, delayed-feedback or survival modeling, or response modeling.
- Strong evaluation judgment, including temporal validation, calibration, and segment-level performance.
- Experience addressing sparse observations, cold start, seasonality, distribution shift, selection bias, or partially observed labels.
- Strong programming and software engineering skills, including building and operating production ML systems.
- Ability to lead ambiguous technical initiatives, make practical tradeoffs, and develop other engineers.
- Preferred experience in advertising inventory, marketplace demand, traffic, retail demand, or capacity forecasting.
- Preferred experience with auction simulation or replay, bid or budget response curves, constrained optimization, counterfactual evaluation, reach estimation, or advanced predictive methods.
Responsibilities
- Lead forecasting and scenario modeling for delivery, reach, performance, and investment tradeoffs.
- Combine forecasts of advertising opportunities, audience activity, marketplace competition, and predicted response.
- Develop models estimating outcomes under changes in budget, bid, audience, and optimization objective.
- Incorporate creative-performance estimates to assess potential gains in delivery, efficiency, or advertiser value.
- Build models that generalize to sparse segments, cold starts, delayed feedback, and changing conditions.
- Evaluate numerical accuracy, calibration, uncertainty, and cohort-level performance.
- Compare recorded predictions with mature outcomes, diagnose systematic errors, and validate estimates through experiments and observed results.
- Partner with engineers on reliable training, serving, monitoring, and iteration; lead designs and mentor engineers.
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