Next Generation of Amazon Search
Amazon is the 4th most popular site in the US (source:
http://www.alexa.com/topsites/countries/US). Our product search engine,
one of the most heavily used services in the world, indexes billions of
products and serves hundreds of millions of customers world-wide. We are
working on a new initiative to transform our search engine into a
shopping engine that assists customers with their shopping
missions. We're looking at every aspect of search, from query
understanding to front-end UX, ranking and relevance, indexing and
tiering and asking how we can make big, step improvements by applying
advanced Machine Learning (ML) and Deep Learning (DL) techniques. This is a rare opportunity to develop cutting edge ML solutions and apply
them to a search problem of this magnitude. Some exciting questions that
we expect to answer over the next few years include:
* Can we deeply understand customer intent and personalize their search
experience even when they type broad queries such as "dress" or
* Can we reduce the cost of serving customer queries on Amazon by two
orders of magnitude using ML to predict n-grams and tuples that many
queries decompose into, apply expensive ranking functions offline to
identify the most relevant products that match these terms, and index
these for efficient online retrieval? We expect this to lead to exciting
research at the intersection of systems and ML.
* Can we deeply understand the catalog to surface products that offer
the most value to a customer? The challenge here is that the definition
of value is subjective and personal, and therefore requires a deeper
understanding of the customers intent as well as preferences.
* Can we increase the experimental velocity of Customer Experience (CX)
experiments by two orders of magnitude? Achieving this will enable us to
rapidly try various CX treatments, and contextualize the CX based on
factors such as customer intent and device.
* Can we use deep learning to transfer behavioral signals from
frequently purchased products in the head to products in the tail where
behavioral signals are sparse? The challenge here is the scale, and the
fact that the head and torso contain only a small fraction of products
while the tail contains an overwhelmingly large fraction of the products
in the catalog.
We are looking to hire ML Applied Scientists at all levels, with experience in Search, Personalization, NLP, Systems, ML, DL and UI Design. Internship opportunities are also available throughout the year and we are flexible about duration and start dates. You will be working alongside world-class researchers and engineers to build next generation search systems and will be able to deploy your ML models into production. Our team is proud of its collaborative and open research environment, where long term thinking and risk taking are highly rewarded. We value academic collaborations and encourage our scientists and engineers to participate and publish in top conferences such as NIPS, ICML, KDD, SIGIR and WWW.
Positions are available in the new Amazon office in Berkeley, CA.
Amazon is a company operating a marketplace for consumers, sellers, and content creators.