Senior Applied Scientist, AI & LLMs for Next-Gen Shopping

Amazon.com Services LLC; Seattle (WA); On-site; USD 136,000 - 184,000; Full time

Benefits offered by this job

Health insurance

401(k) matching

Paid time off

Parental leave

RSUs

Job summary

Amazon.com Services LLC is seeking an Applied Scientist to shape the next generation of AI-driven customer experiences. You will build LLM-powered solutions for the BuyForMe product, enabling customers to discover and purchase from merchants via browser-based agents.

You will develop state-of-the-art models, collaborate with engineers and product teams, and run experiments with real customer data to improve pre-, in-, and post-purchase workflows while ensuring safety and reliability.

Qualifications

• Experience programming in Java, C++, Python or related language.
• Experience with SQL and an RDBMS (e.g., Oracle) or Data Warehouse.
• Master's degree in computer science, mathematics, statistics, machine learning or equivalent quantitative field

Responsibilities

• Invent, implement, and evaluate state-of-the-art models and agentic systems that directly impact customer experience.
• Conduct research that may lead to publications, patents, or cross-Amazon technical influence.
• Collaborate with engineers, product managers, and other scientists to translate business challenges into scalable science solutions.
• Run experiments with real customer data and validate hypotheses in a high-impact product environment.
• Drive excellence in model performance, safety, reliability, and evaluation frameworks.

Skills

Java

C++

Python

SQL

Education

Master's degree in Computer Science, Mathematics, Statistics, Machine Learning or equivalent quantitative field

Tools

Oracle

Job description

Amazon.com Services LLC is seeking an Applied Scientist to shape the next generation of AI-driven customer experiences. You will build LLM-powered solutions for the BuyForMe product, enabling customers to discover and purchase from merchants via browser-based agents.

You will develop state-of-the-art models, collaborate with engineers and product teams, and run experiments with real customer data to improve pre-, in-, and post-purchase workflows while ensuring safety and reliability.