Sr. Applied Scientist, Prime Video - Personalization and Discovery Science
About the role
Prime Video is a first-stop entertainment destination offering customers a vast collection of premium programming in one app available across thousands of devices. Prime members can customize their viewing experience and find their favorite movies, series, documentaries, and live sports – including Amazon MGM Studios-produced series and movies; licensed fan favorites; and programming from Prime Video add-on subscriptions such as Apple TV+, Max, Crunchyroll and MGM+. All customers, regardless of whether they have a Prime membership or not, can rent or buy titles via the Prime Video Store, and can enjoy even more content for free with ads.
Key job responsibilities
- Develop AI solutions for various Prime Video Recommendation and Personalization systems using Deep learning, GenAI, Reinforcement Learning, and optimization methods;
- Work closely with engineers and product managers to design, implement and launch AI solutions end-to-end;
- Design and conduct offline and online (A/B) experiments to evaluate proposed solutions based on in-depth data analyses;
- Effectively communicate technical and non-technical ideas with teammates and stakeholders;
- Stay up-to-date with advancements and the latest modeling techniques in the field;
- Publish your research findings in top conferences and journals.
About the team
Prime Video Recommendation Science team owns science solutions to power recommendation and personalization experience on various Prime Video surfaces and devices. We work closely with the engineering teams to launch our solutions in production.
Basic Qualifications
- PhD, or Master's degree and 6+ years of applied research experience
- 5+ years of building machine learning models for business application experience
- Experience programming in Java, C++, Python or related language
- Experience with neural deep learning methods and machine learning
Preferred Qualifications
- Experience with modeling tools such as R, scikit-learn, Spark MLLib, MxNet, Tensorflow, numpy, scipy etc.
- Experience with large scale distributed systems such as Hadoop, Spark etc.