Principal ML Scientist

Principal / Sr. Principal ML Scientist (Causal Inference, Reinforcement Learning, Ranking & Bid Optimization)

Role OverviewWe are looking for a Principal / Sr. Principal Applied ML Scientist to lead the development of next-generation machine learning systems powering recommendations, search, ads ranking, and monetization platforms at scale. This is a high-impact Individual Contributor (IC) role requiring deep expertise in causal inference, unbiased learning systems, reinforcement learning, and large-scale optimization techniques that improve long-term user engagement, relevance, and business outcomes.The ideal candidate will combine strong hands-on technical depth with cross-functional influence, driving architecture, research direction, and ML best practices across Ads, Recommendations & Personalization, and Search pods.

Key ResponsibilitiesLead the design and deployment of advanced ML systems for recommendations, search, and ads monetization at large scale.Drive research and productionization of applied causal inference techniques for ranking and recommendation systems, including:Unbiased Learning-to-RankCounterfactual/offline evaluationIncrementality measurementPosition bias estimation and mitigationTreatment effect modelingBuild and optimize Reinforcement Learning (RL) frameworks for long-term optimization across user engagement, retention, and monetization objectives.Develop scalable solutions for Cold Start and Long Tail discovery problems using:Embedding-based retrieval systemsExploration/exploitation strategiesCatalog-wide optimizationRepresentation learning techniquesLead innovations in Ads Ranking and marketplace optimization, including:Bid optimizationAuction-aware ML systemsBudget pacingAttribution modelingSimulation frameworksMulti-objective optimization balancing revenue, relevance, user experience, and long-term valueArchitect robust experimentation and evaluation frameworks for measuring model impact reliably in dynamic environments.Act as a technical mentor and thought leader across Ads, Recommendations & Personalization, and Search pods by:Guiding senior engineers and scientists on ML architecture and experimentationDriving best practices for causal inference and evaluationInfluencing roadmap and technical strategy across teamsContribute as a hands-on technical leader through model development, experimentation, system design, and productionization.

Preferred Qualifications10+ years of experience in Machine Learning, Recommender Systems, Search, Ads, or Marketplace Optimization.Deep expertise in causal inference and counterfactual learning applied to large-scale recommendation/search/ads systems.Strong hands-on experience with Reinforcement Learning for production recommendation or monetization systems.Proven experience building large-scale ranking, retrieval, and personalization systems.Strong understanding of:Learning-to-RankBandits and exploration strategiesRepresentation learning / embeddingsAuction systems and ads marketplacesMulti-objective optimizationDemonstrated ability to influence technical direction and drive execution in a highly cross-functional environment without direct people management responsibility.

Good to HaveExperience building ML systems for Notifications, Engagement, or CRM platforms, including:Send-time optimizationCross-channel orchestrationPersonalized content optimization

What Makes This Role ExcitingOpportunity to solve cutting-edge problems at the intersection of causal inference, RL, personalization, and marketplace optimization.Direct impact on large-scale user experience, discovery, engagement, and monetization systems.Ability to influence ML strategy and platform evolution across multiple high-impact domains.Work with high-scale, high-dimensional datasets and state-of-the-art ML infrastructure.