Senior Applied Scientist, Sponsored Products

Key job responsibilities

• Serve as the technical leader in Machine Learning and Generative AI, driving efforts within this team and across other teams.
• Lead end-to-end ML projects with high ambiguity, scale, and complexity—from problem definition to production.
• Build, optimize, and deploy ML models into production, partnering with software engineers to productionize solutions.
• Establish scalable, automated processes for data analysis, model development, validation, and serving.
• Apply strong knowledge of LLMs (prompt engineering, fine-tuning, RAG, evaluation) to build production-grade GenAI applications.
• Analyze large-scale data sets to develop insights that increase traffic monetization and merchandise sales without compromising the shopper experience.
• Design and run A/B experiments, and perform statistical analysis to measure impact and guide decisions.
• Research and prototype innovative ML and GenAI approaches, bringing state-of-the-art techniques into production.
• Recruit, mentor, and grow Applied Scientists on the team.

About The Team

The Sponsored Products Search Sourcing Science (SPSSS) team's mission is to retrieve all relevant sponsored products in response to shopper queries, serving billions of daily ad impressions and tens of millions of clicks, helping shoppers discover useful and contextually relevant products while enabling advertisers to reach the right shoppers in the right context. To achieve this, we build state-of-the-art capabilities spanning query, shopper, product, and advertiser understanding, as well as advanced retrieval, targeting, and ranking systems, all powered by efficient large-scale data pipelines, deep learning, natural language processing (NLP), generative AI, and multi-agent workflows. It's a high-impact, technically exciting space where science directly translates into measurable outcomes for hundreds of millions of customers and millions of advertisers.

Basic Qualifications

• 3+ years of building machine learning models for business application experience
• PhD, or Master's degree and 6+ years of applied research experience
• Experience programming in Java, C++, Python or related language
• Experience with neural deep learning methods and machine learning
• Experience in building speech recognition, machine translation and natural language processing systems (e.g., commercial speech products or government speech projects)

Preferred Qualifications

• Experience with large scale machine learning systems such as profiling and debugging and understanding of system performance and scalability
• Master's degree or above in engineering, statistics, computer science, mathematics, or a related quantitative field
• Experience in patents or publications at top-tier peer-reviewed conferences or journals
• 3+ years of building large-scale machine-learning infrastructure for online recommendation, ads ranking, personalization or search experience
• Experience in machine learning, data mining, information retrieval, statistics or natural language processing, or experience leading engineering teams as a mentor or tech lead
• Experience creating and delivering written and oral communications for technical and non-technical audiences
• Experience in data science, business analytics, business intelligence, or similar experience in big data environments
• Thinks strategically, but stays on top of tactical execution. Exhibits excellent business judgment; balances business, product, and technology very well.
• Experience in computational advertising.
• Experience in Large Language Models (LLMs).