DESCRIPTION

Amazon Advertising is one of Amazon's fastest growing and most profitable 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.

Our products and solutions are strategically important to enable our Retail and Marketplace businesses to drive long-term growth. 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 aims to democratize access to high-quality creatives (audio, images, videos, description) by building AI-driven solutions for advertisers. To accomplish this, we are investing in latent-diffusion models, large language models (LLM), generative audio (music and speech synthesis), computer vision (CV), reinforced learning (RL) and related methods.
You will be part of a close-knit team of applied scientists, product managers, other MLEs and stakeholders who are highly collaborative and at the top of their respective fields.

We are looking for talented Machine Learning Engineer who are adept at a variety of skills, that enable deployment and productization of Generative AI models for advertising at scale.

Every member of the team is expected to build customer (advertiser) facing features, contribute to the collaborative spirit within the team, productize, deploy at scale, and bring cutting edge production-grade research to raise the bar within the team.



Key job responsibilities
As a MLE in the team you will:
- Work closely with Applied Scientists and other MLE and Engineering team members to transform research code into production.
- Own end to end deployment at scale of Generative and ML methods.
- Establish scalable, efficient, automated processes for large-scale data analysis, machine-learning model development, model validation and serving.
- Research new and innovative approaches to do efficient model deployment and training.
- Document processes and methods for technical and non technical audiences.
- Contribute to code reviews.
- Mentor and help recruit MLEs to the team.
- Present outcomes and explain approaches to senior leadership.