Description

　　Prime Video is building the future of TV for customers in over 200 countries to enjoy an endless selection of movies, shows and sports on their TV, mobile devices and desktop. We deliver high-quality video to Amazon customers through subscriptions (Amazon Prime, and Channels) as well as purchases, rentals, and free with ads.

　　In Prime Video Personalization and Discovery, our mission is to show a customer the right video content, in the right place, at the right time. We tailor PV for a diverse and global audience, so customers world-wide with different tastes and backgrounds can find something to watch and enjoy on every visit. We delight Prime subscribers by leaning in to the Amazon originals and exclusives that shape our brand and differentiate us. We grow our relationships with customers by leveraging our deep understanding of them to provide relevant and timely recommendations. We believe the current customer experience is only scratching the surface of innovative experiences that are made possible as viewing continues to shift online, and that science is at the center of delivering a step-function change for our CX.

　　As a Senior Principal Scientist on Prime Video you will have deep subject matter expertise in the area of recommendations science. You will work with multiple teams of scientists and engineers to translate business and functional requirements into concrete deliverables. You will lead ground-breaking efforts in Generative AI to develop new approaches to Personalization, and provide thought leadership to scientists and engineers to invent and implement scalable ML recommendations supporting new Customer Experiences. You will develop solutions that can also be leveraged by organizations across Prime Video and partners including IMDB, represent Prime Video at internal and external science conferences, and work closely with peers in Retail, Music and AWS to advance state of the art research and science application for recommendations. Finally you will work with academic partners to support our in-house talent with direct access to cutting edge research and mentoring.

　　We consider our position as a once-in-a-lifetime opportunity to shape the future of TV for billions of viewers worldwide. We know our future success is inextricably tied to being a center of excellence in machine learning science and we invest in it—we are early adopters of cutting-edge tech and publish papers both internally within Amazon and externally to conferences. This role offers applied science at its best— Generative AI at scale based on rich Amazon datasets, advancing the future of recommendations science, massive customer experience impact in shaping the future of online TV, and clear impact to business KPIs.

　　We are open to hiring candidates to work out of one of the following locations:

　　Seattle, WA, USA

　　Basic Qualifications

　　Graduate degree in Computer science/Math or related field.

　　Experience in building complex, real-time systems involving AI, ML, NLP, Search Systems, Ads with successful delivery to customers.

　　Demonstrated track record of project delivery for large, cross-functional projects with evolving requirements. Ability to take a project from requirements gathering and design to actual product launch

　　Computer Science fundamentals in data structures, algorithm design and complexity analysis.

　　Ability to develop machine learning platform strategy in the subdomain of recommender systems for streaming services.

　　Exceptional customer relationship skills including the ability to discover the true requirements underlying feature requests, recommend alternative technical and business approaches, and lead science efforts to meet aggressive timelines with optimal solutions.

　　Demonstrated track record of peer-reviewed scientific publications that advance state-of-the art for applied science.

　　Preferred Qualifications

　　PhD degree in Computer Science or related field.

　　Experience working on recommender systems such as in e-commerce, advertising, music, video or other fields.

　　Demonstrated ability to push the envelope in such domains as deep learning, NLP, causal learning and bandit learning.

　　Expertise in large language models or demonstrated ability to develop this expertise quickly.

　　Experience in Computer Science fundamentals such as object-oriented design, algorithm design, data structures, problem solving, and complexity analysis.

　　Work with academic partners to support our in-house talent with direct access to cutting edge research and mentoring.

　　Work across Amazon to advance state of the art research and science application in recommendations.

　　More than 15+ years of business/academic experience in building machine learning models.

　　Excellent written and verbal technical communication with an ability to present complex technical information in a clear and concise manner to a variety of audience.

　　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.

　　Our compensation reflects the cost of labor across several US geographic markets. The base pay for this position ranges from $240,100/year in our lowest geographic market up to $350,000/year in our highest geographic market. Pay is based on a number of factors including market location and may vary depending on job-related knowledge, skills, and experience. Amazon is a total compensation company. Dependent on the position offered, equity, sign-on payments, and other forms of compensation may be provided as part of a total compensation package, in addition to a full range of medical, financial, and/or other benefits. For more information, please visit https://www.aboutamazon.com/workplace/employee-benefits. Applicants should apply via our internal or external career site.