Manager, Applied Science, GenAI, Amazon Advertising Job at Amazon in Sunnyvale

Manager, Applied Science, GenAI, Amazon Advertising

Job ID: 2788565 | Amazon.com Services LLC

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 org within the Amazon Advertising team aims to democratize access to high-quality creative assets, including copy, images, and video, by building and productizing generative AI-driven tools for advertisers. We are investing in latent-diffusion and DiT models, LLMs, computer vision, reinforcement learning, and image + video synthesis. The solutions we develop will be deployed for use by self-service advertisers and agencies, as well as available to premium brands that advertise on Amazon.

We are seeking an experienced science leader who is adept at a variety of skills, especially in generative AI, computer vision, and large language models that will accelerate our plans to generate high-quality creatives on behalf of advertisers. The right candidate will be an inventor at heart, provide science leadership, establish the right direction and vision, build team mechanisms, foster the spirit of collaboration and innovation within the org, and execute against a roadmap. The leader will provide both technical direction as well as manage a sizable team of scientists. They will need to be adept at recruiting, launching AI models into production, writing vision/direction documents, and building team mechanisms that will foster innovation and execution.

Key Job Responsibilities

This role is focused on leading a science team focused on computer vision, latent diffusion models, and the related foundational models to product generative imagery and videos.

Responsibilities include:

1. Drive end-to-end applied science projects that have a high degree of ambiguity, scale, and complexity.
2. Provide technical/science leadership related to computer vision, large language models, and generative image + video.
3. Research new and innovative machine learning approaches.
4. Recruit high performing Applied Scientists to the team and provide mentorship.
5. Establish team mechanisms, including team building, planning, and document reviews.

Basic Qualifications

• 10+ years of building AI models for business application.
• 4+ years of experience as a science leader or staff/principal level scientist.
• PhD, or Master's degree and 4+ years of CS, CE, ML or related field experience.

Preferred Qualifications

• Advanced degree in Computer Science, Mathematics, Statistics, Economics, or related quantitative field.
• Published research work in academic conferences or industry circles.
• Experience in building large-scale machine-learning models and infrastructure for online recommendation, ads ranking, personalization, or search.
• Effective verbal and written communication skills with non-technical and technical audiences.
• Experience working with large real-world data sets and building scalable models from big data.
• Thinks strategically, but stays on top of tactical execution.
• Exhibits excellent business judgment; balances business, product, and technology very well.
• Experience in computational advertising.

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.

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