Applied Scientist, AI & Generative Systems

Amazon Inc.; Omaha, Northern (NE, KY); Hybrid; USD 136,000 - 184,000; Full time

Benefits offered by this job

Health insurance

401(k) matching

Paid time off

Parental leave

Job summary

Amazon.com Services LLC is seeking an Applied Scientist to advance AI-driven customer experiences for BuyForMe and related products. You will develop and evaluate agentic LLMs, fine-tune models, and validate hypotheses with real data in a high-impact product environment.

The role requires strong ML/NLP foundations, collaboration with product managers and engineers, and contributions to safety and reliability of models across pre-purchase to post-purchase workflows.

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

Education

Master's degree in computer science, mathematics, statistics, machine learning or equivalent quantitative field

Tools

Oracle

Data Warehouse

Job description

Amazon.com Services LLC is seeking an Applied Scientist to advance AI-driven customer experiences for BuyForMe and related products. You will develop and evaluate agentic LLMs, fine-tune models, and validate hypotheses with real data in a high-impact product environment.

The role requires strong ML/NLP foundations, collaboration with product managers and engineers, and contributions to safety and reliability of models across pre-purchase to post-purchase workflows.