Principal Applied Scientist
Core
Architect and productionize large-scale DNN/LLM-enhanced recommenders, scale E2E ML/AI systems, and drive innovation in AI-forward products including agentic content feeds and generative UI.
Role type
Principal Applied Scientist (Recommendation & Ranking)
Builds
Large-scale DNN/LLM-enhanced recommenders, agentic content feed experiences, generative UI, and the Discover AI platform.
Domain
Web-scale recommendation systems, AI/ML, content discovery
Deliverable
production ML models | product features
Required skills
Representation learning, sequence modeling, retrieval/ranking, slate optimization, user behavior analysis, content quality assessment, distributed training/inference, ML infrastructure design, technical leadership, problem framing, methodology selection
Preferred skills
Publications at top AI/ML conferences, software engineering experience, familiarity with ML infra
Technologies
DNN, LLM, feature stores, distributed training frameworks
Responsibilities
Architect and productionize large-scale DNN/LLM-enhanced recommenders; Gather and analyze user signals to interpret and predict user needs; Design and build models that assess content quality and utility; Collaborate with engineering on data contracts, feature stores, and automated rollout/rollback; Mentor peers through design reviews and knowledge-sharing; Translate user engagements into model objectives and product bets.
Seniority
Principal, hands-on IC with technical leadership
