Help authors around the world publish their books, reach readers, and get help when they need it. You'll build and lead our first science team in Bengaluru and shape the assistant authors turn to for answers and the models that keep the Books catalog trustworthy.
Key job responsibilities - Hire, develop, and retain a high-performing team of applied scientists across levels, and set its practices for experimentation, evaluation, reviews, publishing, and patents
Define the science strategy for how we help authors publish, grow, and succeed, balancing near-term launches with long-term bets
Partner with engineering, product, and policy teams to take science from prototype to production and measure its impact on authors
Write design and planning documents for your area, and present options, risks, and tradeoffs to senior leaders so they can decide
Work closely with our scientists in the US to deliver faster for our authors and improve their experience with our services About the team
Kindle Direct Publishing (KDP) lets anyone publish a book. We are the science organization for Amazon's Books business, now building our science teams in Bengaluru. Our team works in three connected areas: a support assistant that answers authors' questions from their own account data,



in every language KDP supports; multimodal models that use book attributes to keep the catalog trustworthy and help authors reach the right readers; and fine?tuned models that power the assistant and catalog models with KDP expertise. We serve authors, from first?time writers to full?time publishers. We value safety before speed and shared foundations over one?off fixes. Scientists here get dedicated time for innovation, which can lead to patents and conference papers.
Basic Qualifications - Master's degree, or Ph D
3+ years of scientists or machine learning engineers management experience
6+ years of building machine learning models or developing algorithms for business application experience
Experience programming in Java, C++, Python or related language Preferred Qualifications - Experience building large language model (LLM)-based agents or multimodal generative AI workflows, including tool use, planning, retrieval-augmented generation, or vision-language models
Experience fine-tuning or post-training models, including with reinforcement learning
Experience with natural language processing (NLP), statistics, data mining, or large-scale retrieval, embeddings, and ranking

📌 Applied Science Manager (Asti)
🏢 Amazon
📍 Asti