Applied Scientist - GenAI for Shopper Insights

Amazon Science; Seattle (WA); On-site; USD 143,000 - 193,000; Full time

Benefits offered by this job

RSUs

401(k) matching

Health insurance

Parental leave

Job summary

Amazon Science's SPB team is seeking an Applied Scientist who loves innovating at the intersection of customer experience, deep learning, generative AI, and high-scale ML systems. You will mine behavioral data with GenAI, build models for shopper insights, and work with engineering to productionize solutions.

You will design and run rigorous experiments, push for scalable training and inference, and explore fine-tuning, RAG, and agentic workflows to optimize ads and the shopper experience on

Qualifications

• 3+ years building models for business applications.
• PhD or MS with 4+ years in CS/CE/ML or related field.
• Experience programming in Java, C++, Python, or related language.
• Experience in algorithms, data structures, parsing, numerical optimization, data mining, parallel and distributed computing, HPC.

Responsibilities

• Leverage Generative AI and LLMs to mine complex behavioral data and derive shopper insights.
• Design and develop scalable ML and GenAI models focused on shopper intent and preference.
• Partner with engineering to deploy end-to-end GenAI solutions into production.
• Drive scalable model training and inference systems for GenAI workloads at scale.
• Design and run rigorous A/B experiments to quantify impact and guide production rollouts.
• Conduct applied research in novel GenAI techniques to optimize shopper experience.

Skills

Java

C++

Python

GenAI/LLMs

Distributed computing

Education

PhD

Master's degree + 4+ years CS/CE/ML

Tools

Unix/Linux

Software development

Job description

Amazon Science's SPB team is seeking an Applied Scientist who loves innovating at the intersection of customer experience, deep learning, generative AI, and high-scale ML systems. You will mine behavioral data with GenAI, build models for shopper insights, and work with engineering to productionize solutions.

You will design and run rigorous experiments, push for scalable training and inference, and explore fine-tuning, RAG, and agentic workflows to optimize ads and the shopper experience on