Amazon Advertising is one of Amazon's fastest growing businesses. Amazon's advertising portfolio helps merchants, retail vendors, and brand owners succeed via native advertising, which grows incremental sales of their products sold through Amazon. The primary goals are to help shoppers discover new products they love, be the most efficient way for advertisers to meet their business objectives, and build a sustainable business that continuously innovates on behalf of customers. We deliver billions of ad impressions and millions of clicks and break fresh ground in product and technical innovations every day! The Creative X team within Amazon Advertising time aims to democratize access to high-quality creatives (audio, images, videos, text) by building AI-driven solutions for advertisers. To accomplish this, we are investing in understanding how best users can leverage Generative AI methods such as latent-diffusion models, large language models (LLM), generative audio (music and speech synthesis), computer vision (CV), reinforced learning (RL) and related. As an Applied Scientist you will be part of a close-knit team of other applied scientists and product managers, UX and engineers who are highly collaborative and at the top of their respective fields.

Drive the invention and development of novel AI Agent architectures and video/image/audio generation models in advertising.

Strong expertise in AI agent evaluation methodologies and complex task decomposition; hands-on experience with AI-powered video generation pipelines;

Deep knowledge in LLM/VLM fine tuning and reinforcement learning.

Work closely and integrate end-to-end proof-of-concept Machine Learning projects that have a high degree of ambiguity, scale and complexity.

Build interface-oriented systems that use Machine Learning models, perform proof-of-concept, experiment, optimize, and deploy your models into production; work closely with software engineers to assist in productionizing your ML models.

Establish scalable, efficient, automated processes for large-scale data analysis, machine-learning model development, model validation and serving.

Mentor and help recruit Applied Scientists to the team.

Present results and explain methods to senior leadership.

Willingness to publish research at internal and external top scientific venues.

Write and pursue IP submissions.