About the job
As a Senior Staff Engineer, you will lead the technical direction for AI-first experiences, including ranking and relevance systems that sit at the core of our ads marketplace and shape how ads are selected, ordered, and personalized in real time across all verticals. You will design and build next-generation AI-first ranking systems using state-of-the-art techniques such as sequence modeling, deep learning, and large language models (LLMs). Your work will span query understanding, user and merchant representation learning, contextual relevance, and multi-objective optimization, balancing advertiser value, consumer experience, and marketplace health at scale.
Responsibilities
Apply state-of-the-art machine learning and LLM techniques to problems across personalization, query understanding, user and content understanding.
Rigorously evaluate ML and LLM models using a combination of offline analysis and online experimentation, designing metrics and experiments that clearly measure quality, impact, and tradeoffs.
Own the full model lifecycle from research to production, including data analysis, model development, evaluation, offline and online A/B testing, and continuous iteration.
Partner closely with product managers, data scientists, and designers to ensure AI-driven systems deliver meaningful, user-facing improvements.
Stay at the forefront of ML and AI innovation by assessing emerging research and translating promising approaches into scalable, production-ready systems.
Qualifications
Minimum
5+ years of experience building, deploying, and scaling ML and AI models for large-scale, user-facing or data-intensive products.
Proficiency in using AI coding tools (e.g., Claude Code, Codex, Cursor) in the full software development lifecycle, including designing, generating code, testing, monitoring and releasing software
BS, MS, or PhD in Computer Science, Engineering, or a related field, or equivalent practical experience.
Deep expertise in one or more of the following areas: deep learning, large language models, information retrieval, ranking and relevance, recommendation systems, natural language processing, or content understanding.
Strong programming skills in Python, Java, or C++, with hands-on experience using ML frameworks such as PyTorch, TensorFlow, or XGBoost.
Extensive experience across the full ML lifecycle, including data analysis, feature engineering, iterative model development, rigorous offline and online evaluation, and ongoing monitoring and improvement.
Strong collaborator and communicator who thrives in fast-paced, cross-functional environments.
Product-minded and impact-driven, with a passion for applying cutting-edge ML and AI techniques to real-world problems.
Preferred
Experience designing and deploying LLM-based systems, including prompt engineering and retrieval-augmented generation (RAG) architectures, Generative RecSys.
Experience solving large-scale, user-centric and content-centric personalization problems, including user modeling, retrieval, ranking, and relevance.
Demonstrated contributions to the ML community through open-source projects, publications, or applied research in areas such as ML, NLP, information retrieval, or related fields.