Software Engineer II, Search Science Data Infra
Summary
Builds and maintains the machine-learning data pipeline and feature store that power Amazon’s product search ranking, query understanding, and personalization systems using distributed systems and AWS services.
Amazon Search creates customer-focused product search solutions and technologies. Whenever a customer visits an Amazon site worldwide and types in a query or browses through product categories, our systems go to work. We delight customers when we accurately understand their intent expressed via a natural language query and/or image, and reflect that understanding throughout the search page - from layout to the search results and navigation. We make shopping effortless by helping customers easily explore our vast selection, narrowing down a myriad of options to a manageable consideration set through a personalized experience while providing key information to make high confidence decisions with low post-purchase regret
Search Science Data Infrastructure (SSDI) team is responsible for delivering high quality and fresh ML model training data, and providing seamless access to all ML artifacts through federated Feature Store infrastructure. This platform provides the ML training data and real-time signals / embeddings for inferencing to Amazon search ranking, matching quality, search economics and also powers live-site features, including search suggestions, query understanding, spelling, search result ranking, and personalization. Furthermore, 450+ teams across Amazon consume our datasets to power analytics and behavioral models. We are located in downtown Palo Alto, a short walk from numerous shops and restaurants, and right across from the Caltrain station.
Key job responsibilities
As an ML Engineer you will:
Lead development of services and infrastructure at the intersection of machine learning, big data, and distributed systems. Our products and services empower hundreds of science teams across Amazon to deliver machine learning at scale for ML model training, Feature engineering and Data quality monitoring. You will be at the center stage for managing machine learning lifecycle and operations using AWS AI services, DL compute resources, and our core search backend services for query understanding, semantic matching, and relevance ranking. You will drive to provide a world class platform for Amazon Search engineers to comprehensively observe and introspect their applications and services both pre and post deployment to our large scale inference services. You will build scalable data-intensive infrastructure that processes huge amounts of logs, catalogs, transactional data, and telemetry signals. By doing so, we enable teams to become more data-driven and build robust and explainable ML services. You will work with partners on data experimentation to advance Amazon product search, making it available across all geographic regions with variety of product search and discovery use cases across many categories.
Search Science Data Infrastructure (SSDI) team is responsible for delivering high quality and fresh ML model training data, and providing seamless access to all ML artifacts through federated Feature Store infrastructure. This platform provides the ML training data and real-time signals / embeddings for inferencing to Amazon search ranking, matching quality, search economics and also powers live-site features, including search suggestions, query understanding, spelling, search result ranking, and personalization. Furthermore, 450+ teams across Amazon consume our datasets to power analytics and behavioral models. We are located in downtown Palo Alto, a short walk from numerous shops and restaurants, and right across from the Caltrain station.
Key job responsibilities
As an ML Engineer you will:
Lead development of services and infrastructure at the intersection of machine learning, big data, and distributed systems. Our products and services empower hundreds of science teams across Amazon to deliver machine learning at scale for ML model training, Feature engineering and Data quality monitoring. You will be at the center stage for managing machine learning lifecycle and operations using AWS AI services, DL compute resources, and our core search backend services for query understanding, semantic matching, and relevance ranking. You will drive to provide a world class platform for Amazon Search engineers to comprehensively observe and introspect their applications and services both pre and post deployment to our large scale inference services. You will build scalable data-intensive infrastructure that processes huge amounts of logs, catalogs, transactional data, and telemetry signals. By doing so, we enable teams to become more data-driven and build robust and explainable ML services. You will work with partners on data experimentation to advance Amazon product search, making it available across all geographic regions with variety of product search and discovery use cases across many categories.