Applied Scientist, Sponsored Products Off-Search Homepage Team

Amazon • Palo Alto, CA

14h • Onsite

About The Position

The Sponsored Products and Brands (SPB) team at Amazon Ads is re-imagining advertising through generative AI, transforming how millions of customers discover products and engage with brands across Amazon.com and beyond. We bridge human creativity with artificial intelligence across the full advertising lifecycle—from ad creation and optimization to performance analysis and customer insights.

Within SPB, the Off-Search team builds ad experiences across surfaces beyond Search—product detail pages, the homepage, and store-in-store pages. Our team is specifically focused on ad and experience expansion on Homepage: growing the number of ad placements, introducing new widget formats, and delivering richer, more personalized ad experiences that integrate naturally with the shopping journey. We partner with Amazon Stores to ensure ads complement organic recommendations—new arrivals, deals, basket-building content, and fast-delivery options—while adapting to shopper preferences, seasonal moments, and diverse page layouts. We operate full stack, from backend retrieval and auction systems to the shopper-facing experience layer.

If you're energized by solving complex challenges at the intersection of ads, personalization, and customer experience, join us in shaping the future of advertising on Amazon's most visited surface.

Requirements

• PhD, or Master's degree and 4+ years of CS, CE, ML or related field experience
• 3+ years of building models for business application experience
• Experience programming in Java, C++, Python or related language
• Experience with Machine Learning and Large Language Model fundamentals, including architecture, training/inference lifecycles, and optimization of model execution, or experience in machine learning, data mining, information retrieval, statistics or natural language processing
• Experience developing and deploying models in real-world production environments.

Nice To Haves

• Proven expertise in Generative AI, foundation models, LLMs, and/or fine-tuning and customization for downstream tasks.
• Hands-on experience in ads ranking, retrieval, recommendation systems, search, or personalization at web scale.
• Deep understanding of multi-modal modeling, few-shot learning, retrieval-augmented generation (RAG), or reinforcement learning from human feedback (RLHF).
• Experience with online experimentation, A/B testing frameworks, and metrics design for advertising or e-commerce.
• Demonstrated ability to communicate complex technical topics clearly to both technical and non-technical audiences.
• Experience in computational advertising, including familiarity with auction theory, ad economics, and advertiser performance metrics.

Responsibilities

• Contribute to the design and development of GenAI, deep learning, multi-objective optimization and/or reinforcement learning empowered solutions to transform ad retrieval, auctions, whole-page relevance, and/or bespoke shopping experiences.
• Collaborate cross-functionally with other scientists, engineers, and product managers to bring scalable, production-ready science solutions to life.
• Stay abreast of industry trends in GenAI, LLMs, and related disciplines, bringing fresh and innovative concepts, ideas, and prototypes to the organization.
• Contribute to the enhancement of team’s scientific and technical rigor by identifying and implementing best-in-class algorithms, methodologies, and infrastructure that enable rapid experimentation and scaling.
• Mentor and grow junior scientists and engineers, cultivating a high-performing, collaborative, and intellectually curious team.

Benefits

• health insurance (medical, dental, vision, prescription, Basic Life & AD&D insurance and option for Supplemental life plans, EAP, Mental Health Support, Medical Advice Line, Flexible Spending Accounts, Adoption and Surrogacy Reimbursement coverage)
• 401(k) matching
• paid time off
• parental leave
• sign-on payments
• restricted stock units (RSUs)

What This Job Offers

• Job Type: Full-time
• Career Level: Senior