Applied Scientist, Personalization, Personalization

Amazon Science; Illinois; On-site; USD 150,000 - 230,000; Full time

Job summary

Amazon Science is seeking an Applied Scientist to advance customer memory and personalization systems. You will build ML/LLM-powered solutions, extracting and applying customer knowledge to power real-time personalization across the shopping experience.

You will own end-to-end delivery from problem formulation to production deployment, tackling information extraction, memory quality, temporal reasoning, and grounded personalization at scale.

Qualifications

• Proven programming experience in one or more of C/C++, Python, Java, Perl.
• Experience communicating with leadership and cross-functional teams.
• Patents or publications in peer-reviewed conferences or journals is preferred.

Responsibilities

• Design and build ML/LLM-powered personalization systems for customer memory.
• Develop end-to-end ML solutions from problem framing to production deployment at scale.
• Collaborate with engineers and product teams to translate research into measurable impact.

Skills

C/C++

Python

Java

Perl

Education

Publications in top-tier conferences

Tools

Spark

AWS

Job description

Description

We are seeking an Applied Scientist to help build 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 Scientist, you will design and build ML and LLM-powered solutions for Amazon's customer memory and personalization systems. You will work on 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 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.

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

Basic Qualifications

• 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
• Experience communicating clearly and concisely with leadership, stakeholders, and cross-functional teams

Preferred Qualifications

• 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, etc.)
• 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.

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