Sr. Applied Scientist, Sponsored Products - Agentic Bidding Platform

Amazon.com, Inc.•New York, NY

53d

About The Position

We are looking for a passionate Applied Scientist to help pioneer the next generation of Bidding Controls, Recommendations and Insights powering agentic AI applications for Amazon advertisers. In this role, you will design agentic architectures, develop tools and datasets, and contribute to building systems that can reason, plan, and act autonomously across complex advertiser workflows. You will work at the forefront of applied AI, developing methods for fine-tuning, reinforcement learning, and preference optimization, while helping create evaluation frameworks that ensure safety, reliability, and trust at scale. You will work backwards from the needs of advertisers-delivering customer-facing products that directly help them create, optimize, and grow their campaigns. Beyond building models, you will advance the agent ecosystem by experimenting with and applying core primitives such as tool orchestration, multi-step reasoning, and adaptive preference-driven behavior. This role requires working independently on ambiguous technical problems, collaborating closely with scientists, engineers, and product managers to bring innovative solutions into production. The Sponsored Products and Brands team at Amazon Ads is re-imagining the advertising landscape through latest 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. Amazon matches advertisers with shoppers through real-time auctions where advertisers bid to show ads based on what shoppers are currently browsing or searching for. More than 1.5 million Sponsored Products and Brands advertisers use bids to value the true price they want to pay to win an impression for their campaign. The Agentic Bidding Platform (ABP), provides these advertisers with suggested bid value, bidding controls to boost bids and performance estimates for their ad campaigns. Our vision is to improve advertiser experience, through guided bidding controls, to drive more conversions and improve key advertiser metrics. We're building frontier AI solutions that empower advertisers to reach the right shoppers at the right time. Our team combines machine learning with deep shopping insights to create performant bidding tools that drive remarkable results. We have the freedom to innovate and experiment in this rapidly growing space, making a real impact on millions of advertisers and shoppers. If you're excited about shaping the future of e-commerce advertising through advanced technology, this is your opportunity to make a difference at scale.

Requirements

PhD, or Master's degree and 6+ years of applied research experience
3+ years of building machine learning models for business application experience
Experience programming in Java, C++, Python or related language
Experience with neural deep learning methods and machine learning

Nice To Haves

Experience with modeling tools such as R, scikit-learn, Spark MLLib, MxNet, Tensorflow, numpy, scipy etc.
Experience with large scale distributed systems such as Hadoop, Spark etc.

Responsibilities

Lead business, science and engineering strategy and roadmap for Sponsored Products Agentic Bidding Platform.
Design and build agents to guide advertisers to set bids and controls.
Use Large language models to build shopper profiles and shopper cohorts for bid adjustments
Design and implement advanced model and agent optimization techniques, including supervised fine-tuning, instruction tuning and preference optimization (e.g., DPO/IPO).
Curate datasets and tools for MCP.
Build evaluation pipelines for agent workflows, including automated benchmarks, multi-step reasoning tests, and safety guardrails.
Develop agentic architectures (e.g., CoT, ToT, ReAct) that integrate planning, tool use, and long-horizon reasoning.
Tune LLM's to personalize bidding control suggestions for advertisers
Collaborate with peers across engineering and product to bring scientific innovations into production.
Design and develop advanced machine learning algorithms for real-time advertising bid optimization
Innovate on bidding control systems that provide advertisers with precise performance estimates
Analyze complex shopping behavior data to generate actionable insights
Collaborate with cross-functional teams to enhance advertising technology platforms