Sr. Applied Scientist, Amazon Publisher Monetization - Ads

Advertising is a complex, multi-sided market with many technologies at play within the industry. The industry is rapidly growing and evolving as viewers are shifting from traditional TV viewing to streaming video and publishers are increasingly adding video content to their online experiences. Amazon’s video advertising is a rising competitor in this industry. Amazon’s service has differentiated assets in our customer & audience insights, exclusive video content, and associated inventory that position us well as an end-to-end service for advertisers and agencies. We are innovating at the intersection of advertising, e-commerce, and entertainment.

Amazon Publisher Monetization (APM) is looking for a a passionate and experienced scientist who is adept at a variety of skills; especially in generative AI, computer vision, and large language models that will accelerate our plans to maximize yield via AI-driven contextual targeting, Ads syndication and more. The ideal candidate will be an inventor at heart, they will provide science expertise, rapidly prototype, iterate, and launch, foster the spirit of collaboration and innovation within our larger sister teams and their scientists, and execute against a compelling product roadmap designed to bring AI-led science innovation to solve one of the most challenging problems in advertising.

Key job responsibilities
This role is focused on shaping our approach to the solving the trifecta of advertising - serving the right ad to the right viewer at the right moment - delivering engaging ads for viewers, improved performance for advertisers, and maximizing the yield of our supply inventory.

Responsibilities include:
* Partner deeply with Product and Engineering to develop AI-based solutions to generating contextual signals across both video (VOD and Live) and display ads.
* Drive end-to-end applied science projects that have a high degree of ambiguity, scale, complexity.
* Provide technical/science leadership related to computer vision, large language models and contextual targeting.
* Research new and innovative machine learning approaches.
* Partner with Applied Scientists across the broader org to make the most of prior art and contribute back to this community the innovation that you come up with.