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Description

About Sponsored Products and Brands
The Sponsored Products and Brands team at Amazon Ads is re-imagining the advertising landscape through generative AI technologies, revolutionizing how millions of customers discover products and engage with brands across Amazon.com and beyond. We are at the forefront of re-inventing advertising experiences, bridging human creativity with artificial intelligence to transform every aspect of the advertising lifecycle from ad creation and optimization to performance analysis and customer insights. We are a passionate group of innovators dedicated to developing responsible and intelligent AI technologies that balance the needs of advertisers, enhance the shopping experience, and strengthen the marketplace. If you're energized by solving complex challenges and pushing the boundaries of what's possible with AI, join us in shaping the future of advertising.

About our team
The SPB Offsite team builds solutions to extend campaigns to reach customers off the store and extend shopping experiences on third party sites where shoppers search and discover products. We use industry leading machine learning, high scale low latency systems, and AI technologies to create better sponsored customer experiences off the store. This role will have deep interest in building the next innovations in ad tech and shopping wherever shoppers go. You will work with external and internal partners to connect ad tech systems, understand customers, and drive results at scale. You are a deeply technical leader who operates with a GenAI first approach to product, engineering, and science based solutions.

Key job responsibilities
- Lead science and engineering needs across ad systems and models that power sponsored products ads for offsite shopping experiences.
- Collaborate with peers across engineering and product to bring scientific innovations into production.
- Surface qualitative and quantitative insights to shape product direction and ensure product-market fit.
- Design and implement advanced model and agent optimization techniques, including supervised fine-tuning, instruction tuning and preference optimization.
- Develop agentic architectures (e.g., CoT, ToT, ReAct) that integrate planning, tool use, and long-horizon reasoning.
- Prototype and iterate on multi-agent orchestration frameworks and workflows.
- Stay current with the latest research in LLMs, RL, and agent-based AI, and translate findings into practical applications.