Senior Machine Learning Engineer (Ads Optimization)

Reddit; United States; On-site; USD 140,000 - 200,000; Full time

Benefits offered by this job

Comprehensive health benefits

Flexible vacation & Reddit global days

Family planning funds & 4+ months paid

Personal & professional development

Paid volunteer time off

Workspace & home office benefits

Job summary

Reddit is seeking a hands-on Forecasting ML Engineer to build and operate forecasting models that help advertisers understand campaign potential and investment tradeoffs. You will own problem formulation, technical design, evaluation, deployment, and iteration, collaborating with Product, Data Science, Ads Delivery, Audience, and Creative teams.

Responsibilities include building production forecasting pipelines, improving model generalization, and delivering credible scenario estimates to guide

Qualifications

• Strong foundations in statistical learning, probability, and optimization.
• Experience building and evaluating forecasting or response-prediction models.
• Proficiency in designing and deploying production ML pipelines.
• Experience turning product questions into concrete technical plans and leading projects.
• Demonstrated ability to work across Product, Data Science, Ads Delivery, Audience, and Creative teams.

Responsibilities

• Build and own forecasting models and production capabilities for advertising outcomes.
• Translate advertiser questions into modeling problems involving delivery, reach, performance, and investment tradeoffs.
• Develop approaches for model generalization, including sparse data, seasonality, and changing marketplace conditions.
• Measure forecast accuracy, calibration, uncertainty, and performance across advertiser cohorts.
• Deliver reliable end-to-end forecasting systems with monitoring and iteration.
• Collaborate across functions to design, code-review, and mentor engineers on forecasting projects.

Skills

Statistical learning

Probability

Optimization

Forecasting

Response prediction

Time-series

ML pipelines

Python

Cross-functional

Job description

• Build the machine learning capabilities that help advertisers understand what they could achieve on Reddit and make better investment decisions
• On the Forecasting team, you will develop models that estimate how campaign outcomes could change with different budgets, bids, audiences, creative performance, or objectives
• Your work will connect supply and demand forecasting, audience availability, and response prediction to help advertisers compare alternatives and identify valuable opportunities
• This is a hands on role with end-to-end ownership of forecasting models and production capabilities
• You will drive projects from problem formulation and technical design through evaluation, deployment, and iteration, working closely with engineers and cross-functional partners
• Own forecasting and scenario modeling projects. Translate advertiser questions into modeling problems involving delivery, reach, performance, and investment tradeoffs. Develop technical approaches, make practical tradeoffs, and drive execution
• Connect inventory, demand, and response. Use signals about available advertising opportunities, audience activity, marketplace competition, and predicted response to improve estimates of achievable campaign outcomes
• Model outcomes under alternative choices. Build and improve models that estimate the effects of changes in budget, bid, audience, or optimization objective, accounting for relevant auction dynamics and campaign constraints
• Help advertisers identify opportunity. Incorporate predictions from partner teams, including creative-performance estimates, to evaluate potential improvements in delivery, efficiency, or advertiser value
• Improve model generalization. Develop approaches for sparse segments, cold start, seasonality, delayed feedback, and changing marketplace conditions. Choose methods that balance predictive quality, complexity, and production requirements
• Build trustworthy evaluations. Measure forecast accuracy, calibration, uncertainty, and performance across advertiser cohorts. Document modeling assumptions and limitations, and work with partners to determine which scenario comparisons the evidence supports
• Close the learning loop. Compare predictions with mature outcomes, investigate systematic errors, and use experiments and observed results to improve forecasts and validate scenario estimates
• Deliver reliable production systems. Own training, inference, monitoring, and iteration for your models. Collaborate with Product, Data Science, Ads Delivery, Audience, and Creative teams; contribute to design and code reviews; and mentor engineers through project work
• You independently deliver and operate forecasting capabilities that improve accuracy and calibration for priority advertiser populations. Your models support credible comparisons of campaign alternatives, have clear evaluation and monitoring, and help advertisers make better planning and investment decisions

Benefits

• Comprehensive health benefits
• Flexible vacation & Reddit global days off
• Family planning funds & 4+ months paid parental leave
• Personal & professional development funds
• Paid volunteer time off
• Workspace & home office benefits

Strong foundations in statistical learning, probability, and optimization, with experience working with large-scale behavioral or transactional dataExperience diagnosing and addressing modeling challenges such as sparse observations, cold start, seasonality, distribution shift, selection bias, or delayed labelsStrong programming and software engineering skills, including experience building and operating production ML pipelines and servicesExperience independently designing, shipping, and improving predictive ML systems in advertising, marketplaces, recommendations, search, forecasting, or similarly complex domainsPractical experience in at least one relevant area, such as forecasting, response prediction, probabilistic modeling, multitask learning, or transfer learningDemonstrated ability to turn ambiguous product questions into concrete technical plans, lead projects through rollout, and collaborate effectively across functionsSound evaluation judgment, including temporal validation, segment-level analysis, and an understanding of calibration, uncertainty, and the limits of predictions under changed conditionsDemand, supply, and inventory forecasting: Advertising inventory, marketplace demand, traffic, retail demand, or capacity forecasting, including time-series methods, hierarchical or global models, and multi-horizon predictionAuction and investment modeling: Auction simulation or replay, win-rate and price prediction, bid/budget response curves, or constrained optimizationScenario evaluation: Counterfactual or off-policy evaluation, sensitivity analysis, uncertainty propagation, or combining predictive models to estimate campaign outcomesReach and audience estimation: Population and overlap estimation, reach/frequency models, sampling correction, or saturation modelingProbabilistic and response modeling: Quantile or distributional prediction, calibrated conversion/value prediction, or delayed-feedback modeling