Staff Machine Learning Engineer, Ads Forecasting & Scenarios

1d•$230,000 - $322,000

About The Position

Reddit is hiring a Staff Machine Learning Engineer to build machine learning capabilities that help advertisers understand potential campaign outcomes and make better investment decisions. The role leads models that estimate how delivery, reach, performance, and investment tradeoffs may change with different budgets, bids, audiences, creative performance, or objectives. It connects supply and demand forecasting, audience availability, response prediction, and marketplace simulation to help advertisers compare alternatives and identify missed opportunities. This is a hands-on technical leadership role with ownership of modeling direction, evaluation standards, and reusable production capabilities across advertiser planning and recommendations. The engineer will guide model development and evaluation, establish trustworthy scenario comparisons, close the loop between predictions and mature outcomes, and partner with engineers and cross-functional teams on reliable production training, serving, monitoring, and iteration. Success means accurate, calibrated forecasts across priority advertiser populations; credible comparisons of supported campaign alternatives; reusable capabilities across planning and recommendations; and evidence that advertisers identify valuable opportunities and make better investment decisions.

Requirements

Track record of developing and shipping predictive machine learning systems in advertising, marketplaces, recommendations, search, demand forecasting, or similarly complex domains.
Strong foundations in statistical learning, probability, and optimization, with experience modeling large-scale behavioral or transactional data.
Depth in one or more of probabilistic or hierarchical forecasting, calibrated conversion/value prediction, multitask or transfer learning, delayed-feedback or survival modeling, or response modeling.
Strong evaluation judgment, including temporal validation, calibration, segment-level performance, and assumptions needed to estimate 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 experience building and operating production ML systems.
Demonstrated ability to lead ambiguous technical initiatives, make practical tradeoffs, and develop other engineers.

Nice To Haves

Experience in demand, supply, and inventory forecasting, such as advertising inventory, marketplace demand, traffic, retail demand, or capacity forecasting; relevant methods include probabilistic time-series models, hierarchical/global forecasting, external-signal modeling, and multi-horizon prediction.
Experience in auction and investment modeling, including auction simulation or replay, conditional win-rate and price models, bid/budget response curves, or constrained optimization.
Experience in scenario evaluation, including counterfactual or off-policy evaluation, sensitivity analysis, uncertainty propagation, or composition of multiple predictive models.
Experience in reach and audience estimation, including population and overlap estimation, probabilistic reach/frequency models, sampling correction, or saturation modeling.
Experience with advanced predictive methods such as mixture-of-experts, distributional or quantile prediction, temporal sequence models, or state-space models.

Responsibilities

Lead forecasting and scenario modeling for campaign delivery, reach, performance, and investment tradeoffs; set technical direction and guide team execution.
Combine forecasts of advertising opportunities, audience activity, marketplace competition, and predicted response to estimate achievable delivery and performance.
Develop models that estimate outcomes under alternative budgets, bids, audiences, and optimization objectives, accounting for interactions, auction dynamics, and campaign constraints.
Use creative-performance estimates from partner teams to assess potential gains in delivery, efficiency, or advertiser value.
Build generalizable models to handle sparse segments, cold start, delayed feedback, and changing conditions.
Evaluate numerical accuracy, calibration, uncertainty, and cohort-level performance; define supported scenarios and identify when more evidence or experimentation is needed.
Compare predictions with mature outcomes, diagnose systematic errors, and validate scenario estimates through experiments and observed results.
Partner with engineers on reliable model training, serving, monitoring, and iteration.
Lead technical designs, mentor engineers, and collaborate with Product, Data Science, Ads Delivery, Audience, and Creative teams.

Benefits

Comprehensive healthcare benefits and income replacement programs.
401(k) with employer match.
Global benefit programs that fit employees’ lifestyles, including workspace, professional development, and caregiving support.
Family planning support.
Gender-affirming care.
Mental health and coaching benefits.
Flexible vacation and paid volunteer time off.
Generous paid parental leave.
Equity in the form of restricted stock units; depending on the position offered, commission may also be available.
Medical, dental, and vision insurance for U.S.-based employees.