- Palo Alto, CA
Amazon is the 4th most popular site in the 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 "espresso machine"?
Can we reduce the cost of serving customer queries on Amazon by 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 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 have hired ML experts from leading research labs and academia to spearhead this effort. Our research leaders include Trishul Chilimbi (formerly MSR), Inderjit Dhillon (UT Austin), Guy Lebanon (formerly Georgia Tech), and S.V.N. (Vishy) Vishwanathan (formerly UC Santa Cruz). We are looking to hire Software Development Engineers (SDEs) and 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 with duration and start dates. You will be working alongside world-class scientists 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 Palo Alto, Berkeley, Seattle, Boston, Barcelona, and Tokyo.
The Query Understanding Modeling team is responsible for developing and deploying state of the art machine learning and NLP models to extract semantic information on product search queries issued by millions of Amazon customers each day.
As a member of the Query Understanding Modeling team, you will:
-Define, design and implement key initiatives in the Query Understanding area such as semantic parsing framework, query classification, query rewriting, knowledge graph
-Work closely with other applied scientists, engineers, engineering and product managers to understand the current state of the system and drive new enhancements to the system.
-Write technical reports for scientific research and publish results in top conference.
-Use statistical and machine learning techniques to create scalable text understanding systems
-Build a robust, scalable and an extensible platform which supports large scale data analyses, model development, training, validation and implementation
-Estimate engineering effort, identify dependencies, plan implementation, roll outs, provide updates and work with partner teams on initiatives
You will love this role because you will:
-Work on a world-class Query Understanding service that handles billions of requests per day and is an important component of Amazon Product S
-Learn state of the art in terms of AWS and NLP/ML technology and figure out creative ways to make these work at production scale.
-Gain exposure to the workings of the largest e-commerce search engine and an opportunity to work with a dynamic team to define and develop innovative solutions that will have a direct impact on Amazon product search.
-Work with large data sets to analyze and improve the search experience using various AWS technologies.
Have access to Amazon's vast technical resources to get the job done.
At Amazon Search, you'll experience the benefits of working in a dynamic, entrepreneurial environment, while leveraging the resources of Amazon.com (AMZN), one of the world's leading internet companies. We provide a highly customer-centric, team-oriented environment in our offices located in Palo Alto, California.
• Graduate degree (MS or PhD) in Computer Sciences, Mathematics, ,Statistics or related field with specialization in machine learning or natural language processing.
• 8+ years of hands-on experience in predictive modeling and analysis, and in deploying machine learning / deep learning models
• 8+ years of hands-on experience in programming languages such as Java and Python
• PhD with specialization in speech recognition, natural language processing, or machine learning with at least 5 years of related work experience
• Strong software development skills
• Experience working effectively with science, data processing, and software engineering teams
• Proven track record of innovation in creating novel algorithms and advancing the state of the art
• Entrepreneurial spirit combined with strong architectural and problem solving skills
• Strong publication record
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