Sr Applied Scientist, Support Agent Intelligence and Evaluation

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.

Millions of advertisers rely on Amazon's self-service support experience to resolve issues, unblock campaigns, and grow their business. Our Support Agents team is building the science behind intelligent, conversational support — systems that understand advertiser intent, retrieve the right knowledge, generate accurate answers, and know when to escalate. We serve ~2M monthly active advertisers across dozens of locales and languages, and every percentage point of improvement in resolution quality translates directly into advertiser success and retention.

We are seeking an Applied Scientist who is passionate about building evaluation science, NLP systems, and quality measurement at scale. You will define how we measure "good" — designing LLM-as-a-judge evaluation pipelines, developing our next-generation Issue Resolution Rate (IRR) metrics, and closing the quality gap between English and non-English markets. Your work will directly shape what ships to advertisers and what leadership uses to assess the health of our support experience.

Key job responsibilities

1. Enhance support agent capabilities across the broad suite of Amazon Advertising products — expanding coverage, depth of resolution, and advertiser task completion across Sponsored Products, Sponsored Brands, DSP, AMC, and more
2. Design and own the evaluation framework for agent quality — including automated LLM-based scoring of answer correctness, confidence calibration, and conversation-level resolution signals
3. Develop novel metrics that capture whether advertisers actually got the help they needed (beyond surface-level deflection rates)
4. Build and improve retrieval and generation models that power real-time advertiser interactions under strict latency SLAs
5. Drive multilingual science — improve non-English resolution rates through cross-lingual retrieval, translation quality modeling, and locale-aware evaluation
6. Partner with product, engineering, and business teams to productize research and inform roadmap decisions with data

A day in the life You might start the morning reviewing overnight evaluation results from your LLM-as-a-judge pipeline, then jump into a whiteboard session designing a new resolution metric that captures whether advertisers actually unblocked their campaign. After lunch you're running offline experiments on a cross-lingual retrieval model to close the quality gap for non-English markets, and by end of day you're syncing with engineering on latency trade-offs for next week's A/B test. The constant: your science directly changes the experience millions of advertisers have when they need help.