The Amazon Search team creates customer-focused search solutions and technologies. Whenever a customer visits an Amazon site worldwide and types in a query or browses through product categories, our services go to work. We design, develop, and deploy high performance, fault-tolerant distributed search systems used by millions of Amazon customers every day.
Within Amazon Search, the Relevance team has a mission to solve customer problems that require advancing the state of the art in retrieval and ranking. We work backwards from the customer to create value for them by addressing an underlying, unsolved scientific problem. We deploy our solutions through distributed systems that operate at millisecond latencies at Amazon scale. We strive to publish our solutions and open-source our software so that the broader scientific community can benefit.
As a Sr. Applied Scientist on our team, your role is to leverage your strong background in Computer Science and Deep Learning to help build models that shrink hundreds of thousands of retrieved matches to the few hundred that are subject to the final stage ranking. This role requires a pragmatic technical leader comfortable with ambiguity, capable of summarizing complex data and models through clear visual and written explanations. The ideal candidate will have experience with deep learning models and information retrieval/recommendation systems at scale.
The Relevance team operates out of Amazon's Palo Alto and Seattle offices. You will have an opportunity to influence our goals and mission. Your solutions will impact all of Amazon's retail customers. We are a mix of applied scientists and software engineers who collaborate with other teams within Amazon Search to solve and deploy deep learning solutions at scale.
Key job responsibilities
- Analyze the data and metrics resulting from traffic into Amazon's product search service.
- Design, build, and deploy effective and innovative ML solutions to improve search.
- Evaluate the proposed solutions via offline benchmark tests as well as online A/B tests in production.
- Publish and present your work at internal and external scientific venues in the fields of ML/NLP/IR.
