Applied Scientist 2, Prime Video & Studios CoreTech

Amazon Prime Video is looking for passionate, talented, and inventive Applied Scientists with a strong machine learning background to help build industry-leading Audio, Video and Language technology using GenAI. As part of the AI team in Amazon Prime Video, you will work on developing and fine-tuning LLMs and developing new large multimodal models to advance state-of-the-art in the translation, transcription and generation of multingual cinematic content. You will use the latest GenAI techniques to create and assess cinematic content like videos, audio, and captions, and more.

You will design novel approaches to integrate speech, vision, and text for holistic understanding and generation of content, and Collaborate cross-functionally with product managers, engineers, and domain experts to define and deliver high-impact solutions.

Your work will directly impact millions of our customers in the form of products and services that make use of speech and language technology on Prime Video content.

Key job responsibilities We are looking to hire a top notch scientist with strong machine learning background, sharp research acumen and a hunger for solving ambiguous and challenging science problems. This person will work on developing and fine-tuning LLMs and developing new large multimodal models to advance state-of-the-art in the translation, transcription and generation of multingual cinematic content. They will be required to use the latest GenAI advancements to create and assess cinematic content like videos, audio, and captions, and more. Flavor of problems we are trying to solve: a) generating high quality subtitles in many languages meeting high customer satisfaction bar, b) developing foundation model capable of enabling many down stream tasks in localization and accessibility space, c) generating captions with plot pertinency, character identification etc. d) developing solution for assessing quality of subtitles, captions and metadata unified across different content types, linguistic vs non-linguistic aspects, different languages, and which mimics end user satisfaction while at the same time is explainable.