• Sr Machine Learning Scientist - Prime Video Relevance

    Location US-WA-Seattle
    Posted Date 3 months ago(1/2/2019 8:54 AM)
    Job ID
    Company/Location (search) : Country (Full Name)
    United States
  • Job Description

    Want to watch a movie at the end of a long week, but not sure what to choose? Looking for a new show while you wait for the next season of Game of Thrones to start? So are millions of our Prime Video customers. The Prime Video Relevance team helps customers find relevant videos, channels and topics so they can find content they didn’t even known they were looking for, continuing to surprise them with the depth of our catalog.

    We tailor our recommendations through a variety of machine learning algorithms including deep learning neural networks, that you will help define and extend. We are looking for creative, customer and details obsessed machine learning scientists who can apply the latest research, state of the art algorithms and machine learning to build highly scalable recommendation and personalization systems. You'll have a chance to collaborate with talented teams of engineers and scientists to run these predictions on distributed systems at incredible scale and speed.

    As a member of the Prime Video Personalization organization, you will spend your time as a hands-on machine learning practitioner and a research leader. You will play a key role on the team, building and guiding machine learning models from the ground up. At then of the day, you will have the reward of seeing your contributions benefit millions of Amazon.com customers worldwide.

    Some examples of the things we work on:
    • Using Neural Networks and Deep Learning techniques to find titles that customers will enjoy
    • Build and operate services that deliver millions of recommendations per second
    • Extend models and algorithms to support our ever growing ways of consuming content (subscriptions, live, rentals etc), dealing with unique challenges such as observational bias and rapidly scaling dimensions
    • Constantly experimenting with changes to the underlying algorithms and models to deliver relevant content to a wide variety of customer experiences
    If you are ready to truly make an impact on a product that is used by millions of people around the world, including your own friends and family, then we would love to talk to you.

    Amazon.com is an Equal Opportunity-Affirmative Action Employer – Minority / Female / Disability / Veteran / Gender Identity / Sexual Orientation

    Basic Qualifications

    • PhD in Computer Science, Machine Learning, Operational Research, Statistics or a related quantitative field
    • 5+ years of practical experience applying ML to solve complex problems;
    • Algorithm and model development experience for large-scale applications;
    • Experience using Java, C++, or other programming language, as well as with R, MATLAB, Python or similar scripting language;
    • Experience distilling informal customer requirements into problem definitions, dealing with ambiguity and competing objectives

    Preferred Qualifications

    • PhD in Computer Science (Machine Learning, AI, Statistics, Mathematics, or equivalent);
    • 10+ years of practical experience applying ML to solve complex problems;
    • Significant peer reviewed scientific contributions in premier journals and conferences;
    • Proven track record of innovation in creating novel algorithms and advancing the state of the art
    • Extensive knowledge and practical experience in deep neural networks and other recommendation systems, including: convolutional neural networks (CNNs), recurrent neural networks (RNNs), residual neural networks and collaborative filtering techniques;
    • Expert in more than one more major programming languages (C++, Java, or similar) and at least one scripting language (Perl, Python, or similar);
    • Strong fundamentals in problem solving, algorithm design and complexity analysis;
    • Strong personal interest in learning, researching, and creating new technologies with high customer impact;
    • Experience with defining research and development practices in an applied environment;
    • Proven track record in technically leading and mentoring scientists;
    • Superior verbal and written communication and presentation skills, ability to convey rigorous mathematical concepts and considerations to non-experts.
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