Applied Scientist, Personalization, Personalization

Description

We are seeking an Applied Scientist to help build Amazon’s next-generation customer memory and personalization systems.

Is it beyond just reacting to customer behavior? We are 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 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.

The team plays a central role in defining how Amazon understands its customers and applies that understanding across the shopping experience.

As an Applied Scientist, you will design and build ML and LLM‑powered solutions for customer memory and personalization systems. You will work on how customer knowledge is extracted, validated, and applied in production systems. You will own end‑to‑end delivery of ML solutions, from problem formulation and modeling to offline and online experimentation and production deployment  will deliver 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 collaborate closely with engineering and product teams to translate research into measurable customer impact.

• Knowledge of programming languages such as C/C++, Python, Java or Perl.
• Experience in patents or publications at top‑tier peer‑reviewed conferences or journals.
• PhD, or a Master’s degree and experience in CS, CE, ML or related field research.
• Strong communication and collaboration skills.

• Experience in building and launching deep learning and machine learning models for business applications.
• Solid knowledge of big data and cloud technologies (e.g., Spark, AWS).
• Experience with information retrieval, recommender systems, natural language processing, and/or personalization algorithms.
• Publications at top Web, Machine Learning, Natural Language Processing conferences such as KDD, ICML, NeurIPS, ACL, EMNLP, etc.