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Applied Scientist, Podcasts, Podcasts Machine Learning

Amazon

Our mission: To be Earth's most customer-centric company.

DESCRIPTION

Job summary
Amazon Music reimagines music listening by enabling customers to unlock millions of songs, podcast episodes, and thousands of curated playlists and stations with their voice. Amazon Music provides unlimited access to new releases and classic hits across iOS and Android mobile devices, PC, Mac, Echo, and Alexa-enabled devices including Fire TV and more. With Amazon Music, Prime members have access to ad-free listening of 2 million songs at no additional cost to their membership. Listeners can also enjoy the premium subscription service, Amazon Music Unlimited, which provides access to more than 75 million songs and the latest new releases. Amazon Music Unlimited customers also now have access to the highest-quality listening experience available, with more than 75 million songs available in High Definition (HD), more than 7 million songs in Ultra HD, and a growing catalog of spatial audio. Customers also have free access to an ad-supported selection of top playlists and stations on Amazon Music. All Amazon Music tiers now offer a wide selection of podcasts at no additional cost, and live streaming in partnership with Twitch. Engaging with music and culture has never been more natural, simple, and fun. For more information, visit amazonmusic.com or download the Amazon Music app.

The Podcasts Machine Learning team is responsible for the machine learning and natural language processing models that underly Amazon Music’s podcast recommendations and search experiences on mobile, web and Alexa.

As an Applied Scientist on the team, you will play an integral part driving innovation in recommendations and discovery of podcasts. You will work with a team of product managers, applied scientists, data scientists, and software engineers delivering meaningful recommendations, personalized for each of the millions of customers using Amazon Music globally. You will be involved in every aspect of the development lifecycle, from idea generation and scientific research to development and deployment of advanced models. You will work closely with engineering to realize your scientific vision. You will have many of the following technical and leadership responsibilities:

Key job responsibilities

  • Work backwards from customer needs to research and develop novel machine learning approaches for podcast recommendations and search
  • Design, develop, evaluate, and deploy machine learning (ML), natural language processing (NLP) models and scalable solutions for customer problems
  • Analyze and extract relevant information from large amounts of structured and unstructured, complex and interrelated datasets describing customer behavior, messaging, podcast content
  • Ship production quality code for offline model building and collaborate with software engineering teams to drive implementation and experimentation in complex Amazon production systems
  • Establish scalable, efficient, automated processes for large scale data analyses, model development, and evaluation
  • Identify ML-driven areas of improvement in personalization, recommendation and search products
  • Improve upon existing methods by developing new data sources, testing model enhancements, and fine-tuning model parameters
  • Advocate technical solutions to business stakeholders, engineering teams, as well as executive level decision makers
  • Communicate results, insights and recommendations to audiences of varying levels of technical sophistication
  • Conduct literature reviews and stay current with advancements in the field, adapting latest in literature to build efficient and scalable models
  • Promote the culture of experimentation and scientific rigor at Amazon
  • Contribute to progress of the Amazon and broader research communities by publishing papers

This role can be located in Culver City, CA, San Francisco, CA, or Seattle, WA

BASIC QUALIFICATIONS

  • PhD or equivalent Master’s Degree plus 4+ years of experience in CS, CE, ML or related field
  • 2+ years of experience of building machine learning models for business application
  • Experience programming in Java, C++, Python or related language

PREFERRED QUALIFICATIONS

  • PhD in Computer Science (Machine Learning, AI, Statistics, or equivalent);
  • Ability to distill informal customer requirements into problem definitions, dealing with ambiguity and competing objectives;
  • Extensive knowledge and practical experience in several of the following areas: machine learning, statistics, deep learning, NLP, recommendation systems, information retrieval;
  • Track record of scientific publications in premier journals and conferences;
  • Strong problem solving skills;
  • Experience handling gigabyte and terabyte size datasets;
  • Skilled with Python or similar scripting language; as well as with Java, C++, or other programming language, and one Deep Learning Framework (Pytorch, TensorFlow, MXNet, etc.)
  • Experience with MapReduce and programming tools like Spark
  • Professional experience in software development (software design and development life cycle);
  • Strong verbal and written communication skills, ability to convey rigorous mathematical concepts and considerations to non-experts.

Pursuant to the Los Angeles Fair Chance Ordinance, we will consider for employment qualified applicants with arrest and conviction records.

Pursuant to the San Francisco Fair Chance Ordinance, we will consider for employment qualified applicants with arrest and conviction records.

Amazon is committed to a diverse and inclusive workplace. Amazon is an equal opportunity employer and does not discriminate on the basis of race, national origin, gender, gender identity, sexual orientation, protected veteran status, disability, age, or other legally protected status. For individuals with disabilities who would like to request an accommodation, please visit https://www.amazon.jobs/en/disability/us.

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Applied Scientist, Podcasts, Podcasts Machine Learning

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Amazon

Our mission: To be Earth's most customer-centric company.

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