We are seeking an Applied Science Manager to lead a team building Amazon’s next-generation customer memory and personalization systems.
Are you interested in building systems that move beyond reacting to customer behavior, to actually understanding and remembering it over time? Our team is building Amazon’s customer memory layer – a system that extracts, curates, and reasons over customer knowledge to power next-generation personalization. This includes transforming noisy, unstructured signals into durable, high-quality representations of customer preferences, intents, and life events, and using them in real time to improve customer experiences.
We are part of Amazon’s Personalization organization, a high-performing group that leverages large-scale machine learning, generative AI, and distributed systems to deliver highly relevant customer experiences. We tackle challenging problems at the intersection of information extraction, knowledge representation, LLM reasoning, and recommendation systems. Our systems operate under real-world constraints of scale, latency, and quality, requiring careful tradeoffs between precision, recall, and responsiveness. This team plays a central role in defining how Amazon understands its customers, and how that understanding is applied across the shopping experience. As an Applied Science Manager, you will lead a team of scientists working on LLM-powered memory and personalization systems. You will define the scientific direction for how customer knowledge is extracted, validated, and applied in production systems.
Key job responsibilities As an Applied Science Manager, you will lead a team of scientists working on LLM-powered memory and personalization systems. You will define the scientific direction for how customer knowledge is extracted, validated, and applied in production systems.
You will own the end-to-end delivery of ML solutions, from problem formulation and modeling to offline and online experimentation, and production deployment at scale. You will ensure your team delivers high-quality, scalable systems that power customer-facing experiences. You will drive work across areas such as fact extraction, memory quality and lifecycle, temporal reasoning, and grounded personalization, while navigating tradeoffs between quality, latency, and coverage. You will hire and develop scientists, raise the scientific bar, and foster a culture of ownership and rigor. You will partner closely with engineering and product teams to translate research into measurable customer impact, and represent your team’s work to senior leadership.
Please visit https://www.amazon.science for more information.
Basic Qualifications
- PhD, or Master’s degree and 6+ years of applied research experience in machine learning or a related field
- 3+ years of experience managing Applied Scientists or ML Engineers
- Experience leading teams that own ML solutions end to end, including problem formulation, offline experimentation, online experimentation (A/B testing), and production deployment at scale
- Proven track record of translating ambiguous business problems into ML solutions that delivered measurable customer or business impact
- Experience setting scientific direction, reviewing modeling approaches, and raising the quality bar across a team
- Hands-on experience applying modern machine learning techniques (including deep learning and/or Large Language Models) to real-world problems
- Experience leading cross-team initiatives in ambiguous environments, including defining roadmaps and influencing partner teams
- Strong communication skills, with the ability to influence senior leadership and drive alignment across science, engineering, and product teams
Preferred Qualifications
- Experience with Large Language Models, including training, fine-tuning, adaptation, or large-scale inference
- Experience in one or more of the following: Recommendation Systems, Information Retrieval, NLP, or personalization systems at scale
- Experience with sequential recommendation, user intent or mission modeling, or behavioral modeling
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