Principal Applied Scientist
Core
Lead content-quality understanding at scale and advance the recommendation & ranking stack by designing, deploying, and productionizing large-scale DNN/LLM-enhanced models.
Role type
Principal Applied Scientist (Recommendation & Content Safety)
Builds
Large-scale DNN/LLM-enhanced recommenders, human-in-the-loop pipelines, and safety/trust layers for generative and curated experiences.
Domain
Internet/Consumer Tech, Machine Learning, Recommendation Systems, Content Safety
Deliverable
production ML models
Required skills
LLMs (prompting, finetuning, RAG), multimodal modeling, retrieval-augmented recommendation, counterfactual learning, multi-objective optimization, content integrity/safety systems, cross-disciplinary leadership, mentoring, technical vision.
Preferred skills
Experience with Python, major deep learning frameworks (PyTorch/TensorFlow), large-scale data processing, distributed training/inference.
Technologies
PyTorch, TensorFlow, Python, DNN, LLM, RAG
Responsibilities
Design and deploy models assessing credibility, usefulness, freshness, safety, and diversity; reduce misinformation/toxicity error rates; architect and productionize recommenders; define offline metrics and online methodologies (A/B tests, bandits); partner with policy teams to encode safety standards; scale E2E ML systems; mentor scientists and set technical vision; translate user engagements into model objectives.
Seniority
Principal, hands-on IC with strategy & mentorship
