Lead the design and development of machine learning models for Search ranking, including relevance ranking, personalization, result quality, intent understanding, and engagement optimization
Own major ranking initiatives from problem definition through experimentation, launch, and iteration
Develop and improve ranking models using techniques such as learning-to-rank, deep retrieval, neural ranking, sequence models, embeddings, multi-task learning, calibrated prediction, and large-scale feature engineering
Build ranking systems that balance multiple objectives, such as relevance, user satisfaction, freshness, diversity, fairness, safety, latency, and business goals
Partner with product managers, data scientists, and engineers to define success metrics, experimentation strategy, and long-term ranking roadmap
Analyze user behavior, search logs, query-result interactions, and model performance to identify opportunities for improvement
Design robust offline evaluation, online experimentation, and model monitoring frameworks
Improve feature pipelines, training infrastructure, serving systems, and model iteration velocity
Provide technical leadership across teams, influence architecture decisions, and mentor engineers working on ML ranking systems
Stay current with advances in search, recommendation systems, ads ranking, generative AI, LLM-based ranking, and retrieval-augmented systems
Requirements
Bachelor's Degree in a relevant technical field such as computer science or equivalent years of practical work experience
8+ years of post-Bachelor’s machine learning experience; or Master’s degree in a technical field + 7+ year of post-grad machine learning experience; or PhD in a relevant technical field + 4 years of post-grad machine learning experience