Principal / Sr. Principal ML Scientist (Causal Inference, Reinforcement Learning, Ranking & Bid Optimization)
Role Overview
We 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 Responsibilities
Lead 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:
Build 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 systems
Exploration/exploitation strategies
Catalog-wide optimization
Representation learning techniques
Lead innovations in Ads Ranking and marketplace optimization, including:
Bid optimization
Auction-aware ML systems
Budget pacing
Attribution modeling
Simulation frameworks
Multi-objective optimization balancing revenue, relevance, user experience, and long-term value
Architect 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 experimentation
Driving best practices for causal inference and evaluation
Influencing roadmap and technical strategy across teams
Contribute as a hands-on technical leader through model development, experimentation, system design, and productionization.
Preferred Qualifications
10+ 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-Rank
Bandits and exploration strategies
Representation learning / embeddings
Auction systems and ads marketplaces
Multi-objective optimization
Demonstrated ability to influence technical direction and drive execution in a highly cross-functional environment without direct people management responsibility.
Good to Have
Experience building ML systems for Notifications, Engagement, or CRM platforms, including:
Send-time optimization
Cross-channel orchestration
Personalized content optimization What Makes This Role Exciting
Opportunity 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.