Staff Machine Learning Engineer, Ads Forecasting & Scenarios
Staff-level ML engineer on Reddit's ads team, leading forecasting and scenario models that estimate how campaign outcomes change with different budgets, bids, audiences, and creative. Hands-on technical leadership across probabilistic forecasting, marketplace simulation, and production ML systems.
- Compensation
- $230K–$322K / yr
- Location
- Remote - United States
- Work setup
- Remote
- Seniority
- Staff
- Employment
- Contract
- Experience
- Not specified
- Category
- Ml / AI
- Posted
Skills
Must have
- Track record developing and shipping predictive ML systems in advertising, marketplaces, recommendations, search, demand forecasting, or similar complex domains
- Strong foundations in statistical learning, probability, and optimization; experience modeling large-scale behavioral or transactional data
- Depth in one or more of: probabilistic/hierarchical forecasting, calibrated conversion/value prediction, multitask or transfer learning, delayed-feedback or survival modeling, or response modeling
- Strong evaluation judgment: temporal validation, calibration, segment-level performance, assumptions for estimating outcomes under changed conditions
- 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
- Demonstrated ability to lead ambiguous technical initiatives, make practical tradeoffs, and develop other engineers
Nice to have
- Demand, supply, and inventory forecasting (probabilistic time-series, hierarchical/global forecasting, external-signal modeling, multi-horizon prediction)
- Auction and investment modeling (auction simulation/replay, win-rate and price models, bid/budget response curves, constrained optimization)
- Scenario evaluation (counterfactual/off-policy evaluation, sensitivity analysis, uncertainty propagation)
- Reach and audience estimation (population/overlap estimation, reach/frequency models, sampling correction, saturation modeling)
- Advanced predictive methods (mixture-of-experts, distributional/quantile prediction, temporal sequence models, state-space models)
Full description
Reddit is a community of communities. It’s built on shared interests, passion, and trust, and is home to the most open and authentic conversations on the internet. Every day, Reddit users submit, vote, and comment on the topics they care most about. With 100,000+ active communities and approximately 130 million daily active unique visitors, Reddit is one of the internet’s largest sources of information. For more information, visit .
The opportunity
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.
What you’ll do
- 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.