Sr. Applied Scientist, Prime AI/ML Science

About the role

Join our team of Scientists developing models to understand and optimize customer behavior and experience with Amazon Prime. This role focuses on modeling customer behavior, assessing the long-term value of the Prime membership program, and creating personalized frameworks for content and subscription optimization. You will tackle challenges such as optimizing GenAI/LLM solutions for Prime personalization, building foundation models, global scalability, combinatorial optimization, cold start problems, accelerated experimentation, and multi-step optimization leading to reinforcement learning of the customer journey.

We employ techniques from GenAI/LLMs, supervised/semi-supervised learning, deep learning, transformer architectures, causal econometric modeling, and reinforcement learning. As the central science team within Prime, you will contribute to a strong publication and patent record while utilizing cutting-edge ML technologies and infrastructure, including AWS technologies (EMR/Spark, Sagemaker, DynamoDB, S3, ClaudeCode) and various AI/ML algorithms.

Responsibilities

• Stay abreast of current literature in the field and advance/build novel science solutions leveraging state-of-the-art (SoTA) techniques.
• Build and develop AI/ML models and supporting infrastructure at terabyte scale, in coordination with software engineering teams.
• Leverage deep learning and GenAI solutions for building foundation models and personalized optimization solutions.
• Develop offline policy estimation tools and integrate them with measurement systems and econometric models.
• Establish scalable, efficient, and automated processes for large-scale data analyses, science development, validation, and model implementation.
• Analyze and extract relevant information from large amounts of Amazon’s historical business data to automate and optimize key processes.
• Work closely with business teams to understand problem spaces, identify opportunities, and formulate problems.
• Use AI, machine learning, data mining, and statistical techniques to create actionable, meaningful, and scalable solutions for business problems.
• Design, develop, and evaluate highly innovative models and statistical approaches to understand and predict customer behavior and solve business problems.

Requirements

• 3+ years of experience building machine learning models for business applications.
• PhD or Master’s degree with 6+ years of applied research experience.
• Experience programming in Java, C++, Python, or a related language.
• Experience with neural deep learning methods and machine learning.

Preferred Qualifications

• Experience with modeling tools such as R, scikit-learn, Spark MLlib, MxNet, TensorFlow, NumPy, or SciPy.
• Experience with large-scale distributed systems such as Hadoop or Spark.