Applied Scientist, Prime Video Sports Personalization

We are looking for an Applied Scientist with domain expertise in Personalization, Information Retrieval, and Recommender Systems, or general ML to develop new algorithms and end-to-end solutions. As part of our team of applied scientists and software development engineers, you will be responsible for researching, designing, developing, and deploying algorithms into production pipelines. Your role will involve working with cutting-edge technologies in recommender systems and search. You'll also tackle unique challenges like temporal information retrieval to improve real-time sports content recommendations. As a technologist, you will drive the publication of original work in top-tier conferences in Machine Learning and Recommender Systems. We expect you to thrive in ambiguous situations, demonstrating outstanding analytical abilities and comfort in collaborating with cross-functional teams and systems. The ideal candidate is a self-starter with the ability to learn and adapt quickly in

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Basic Qualifications
- Experience building machine learning models or developing algorithms for business application
- PhD, or a Master's degree and experience in CS, CE, ML or related field research
- Experience programming in Java, C++, Python or related language
- Experience in any of the following areas: algorithms and data structures, parsing, numerical optimization, data mining, parallel and distributed computing, high-performance computing
Preferred Qualifications
- Experience in patents or publications at top-tier peer-reviewed conferences or journals
- Experience creating novel algorithms and advancing the state of the art
- Significant peer-reviewed scientific contributions in premier journals and conferences
- Experience in building production grade deep learning models for Information Retrieval, recommendation systems, or related fields
- Experience with defining research and development practices in an applied environment
- Solid knowledge of big data and cloud technologi