Senior Applied Scientist, Prime Video Sports
DESCRIPTION
Come build the future of entertainment with us. Are you interested in helping shape the future of movies and television? Do you want to help define the next generation of how and what Amazon customers are watching?
Prime Video is a premium streaming service that offers customers a vast collection of TV shows and movies - all with the ease of finding what they love to watch in one place. We offer customers thousands of popular movies and TV shows including Amazon Originals and exclusive licensed content to exciting live sports events. We also offer our members the opportunity to subscribe to add-on channels which they can cancel at anytime and to rent or buy new release movies and TV box sets on the Prime Video Store. Prime Video is a fast-paced, growth business - available in over 240 countries and territories worldwide. The team works in a dynamic environment where innovating on behalf of our customers is at the heart of everything we do. If this sounds exciting to you, please read on.
We are looking for a Senior Applied Scientist to embark on our journey to build a Prime Video Sports tech team in Israel from ground up. Our team will focus on developing products to allow for personalizing the customers’ experience and providing them real-time insights and revolutionary experiences using Computer Vision (CV) and Machine Learning (ML). You will get a chance to work on greenfield, cutting-edge and large-scale engineering and science projects, and a rare opportunity to be one of the founders of the Israel Prime Video Sports tech team in Israel.
Key job responsibilities
We are looking for a Senior Applied Scientist with domain expertise in Computer Vision or Recommendation Systems to lead development of new algorithms and E2E solutions. You will be part of a team of applied scientists and software development engineers responsible for research, design, development and deployment of algorithms into production pipelines. As a technologist, you will also drive publications of original work in top-tier conferences in Computer Vision and Machine Learning. You will be expected to deal with ambiguity! We're looking for someone with outstanding analytical abilities and someone comfortable working with cross-functional teams and systems. You must be a self-starter and be able to learn on the go.
About the team
In September 2018 Prime Video launched its first full-scale live streaming experience to world-wide Prime customers with NFL Thursday Night Football. That was just the start. Now Amazon has exclusive broadcasting rights to major leagues like NFL Thursday Night Football, Tennis major like Roland-Garros and English Premium League to list few and are broadcasting live events across 30+ sports world-wide. Prime Video is expanding not just the breadth of live content that it offers, but the depth of the experience. This is a transformative opportunity, the chance to be at the vanguard of a program that will revolutionize Prime Video, and the live streaming experience of customers everywhere.
BASIC QUALIFICATIONS
- PhD or Master plus experience in Computer Science, Machine Learning or related field.
- Coding and design skills, proficiency with programming languages such as C/C++, Java, or Python.
- Strong CS fundamentals in data structures, problem solving, algorithm design and complexity analysis.
PREFERRED QUALIFICATIONS
- Significant peer-reviewed scientific contributions in premier journals and conferences.
- Experience in building production grade deep learning models for computer vision, recommendation systems, or related fields.
- Experience with defining research and development practices in an applied environment.
- Solid knowledge of big data and cloud technologies (e.g., Spark, AWS, etc.).
- Scientific thinking and the ability to invent, a track record of thought leadership and contributions that have advanced the field.
- Track record in creating novel algorithms and advancing the state of the art
- Technical writing and cross discipline collaboration skills.
- Background in classical CV algorithms is a plus.