Applied Scientist, Customer360

Amazon serves hundreds of millions of customers. Each one has a unique history of purchases, preferences, and behaviors. Our team's mission: turn that history into real-time contextual intelligence that makes every Amazon experience feel personal.

We're hiring an Applied Scientist to push the boundaries of what's possible with LLMs, semantic retrieval, and customer understanding at scale.

The problem space: Imagine a system that can instantly synthesize years of customer signals — what they bought, what they love, what they're planning — and surface the exact right context for any experience, in milliseconds. That's what we build. It's equal parts information retrieval, generative AI, and systems engineering.

1. Scale: Your models will serve 1,500+ requests per second across Amazon's largest surfaces. 2. Impact: Direct revenue attribution in the hundreds of millions — your work shows up in customer experiences the same week. 3. Frontier tech: Fine-tuning LLMs, building custom embedding models, designing retrieval architectures that balance quality with sub-100ms latency constraints. 4. Data richness: Access to one of the most comprehensive customer behavior datasets anywhere. 5. Ownership: End-to-end — from research to production deployment to metric evaluation.

Key job responsibilities 1. Invent new approaches to contextual retrieval, relevance scoring, and LLM-based summarization. 2. Fine-tune and evaluate language models for domain-specific understanding. 3. Design experiments that measure real customer impact, not just benchmark scores. 4. Ship production systems and iterate based on live metrics. 5. Collaborate across teams — Alexa, Search, Recommendations — as a platform that powers them all. 6. Mentor team members and shape the technical direction of our roadmap.

Please visit https://www.amazon.science for more information.

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