Software Development Engineer, DiscoLX
Amazon.com
The Discovery LLM Experiences team (DiscoLX) is looking for Software Development Engineers! We are re-imagining the bookstore experience using Large Language Models and semantic AI to make discovery deeply personalized and meaningful. Our team is building next-generation recommendation systems powered by LLM-driven semantic modeling, embeddings, and large-scale experimentation frameworks. We design and operate scalable services that generate structured book intelligence, power similarity retrieval, enable explainable “why this fits you” experiences, and integrate seamlessly with ranking and behavioral systems at Amazon scale.
As part of the team, you’ll work on challenging distributed systems problems across both backend and frontend surfaces, solving questions like how to encode narrative meaning into machine-usable vectors, how to blend LLM reasoning with ML ranking, and how to make AI systems deterministic, cost-efficient, and production-ready. Your solutions will reach millions of customers globally and directly shape how readers discover and connect with books. We’re looking for engineers with strong technical fundamentals, curiosity about LLM systems and large-scale architectures, and the drive to build durable AI infrastructure in a fast-paced environment. Come help us reinvent book discovery at scale.
As part of the team, you’ll work on challenging distributed systems problems across both backend and frontend surfaces, solving questions like how to encode narrative meaning into machine-usable vectors, how to blend LLM reasoning with ML ranking, and how to make AI systems deterministic, cost-efficient, and production-ready. Your solutions will reach millions of customers globally and directly shape how readers discover and connect with books. We’re looking for engineers with strong technical fundamentals, curiosity about LLM systems and large-scale architectures, and the drive to build durable AI infrastructure in a fast-paced environment. Come help us reinvent book discovery at scale.
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