Applied Scientist, Personalization & Recommendations at Scale

Amazon; Austin (TX); On-site; USD 143,000 - 193,000; Full time

Benefits offered by this job

Health insurance (medical, dental, etc

401(k) matching

Paid time off

Parental leave

Job summary

Amazon is seeking researchers to build state-of-the-art recommendation systems and personalization engines at scale, transforming how millions of customers discover products, content, and experiences. You will work with world-class researchers in AWS AI to develop scalable models and publish findings at peer-reviewed venues.

Responsibilities include advancing large-scale recommendation and ranking models, contextual and real-time personalization, and integration of foundation models and LLMs

Qualifications

• 3+ years of building models for business application experience
• PhD, or Master's degree and 4+ years of CS, CE, ML or related field experience
• Experience in patents or publications at top-tier peer-reviewed conferences or journals
• Experience programming in Java, C++, Python or related language
• Experience in any of the following areas: algorithms and data structures, parsing, numerical optimization, data mining, parallel and distributed computing, high-performance computing
• Experience using Unix/Linux
• Experience in professional software development

Responsibilities

• Build and deliver models for personalized experiences at scale.
• Collaborate with research and engineering teams to advance AWS AI and Amazon Personalize.

Skills

Java

C++

Python

Algorithms and data structures

Parsing

Numerical optimization

Data mining

Parallel computing

Distributed computing

High-performance computing

Unix/Linux

Education

PhD or Master’s + CS/CE/ML

Job description

Amazon is seeking researchers to build state-of-the-art recommendation systems and personalization engines at scale, transforming how millions of customers discover products, content, and experiences. You will work with world-class researchers in AWS AI to develop scalable models and publish findings at peer-reviewed venues.

Responsibilities include advancing large-scale recommendation and ranking models, contextual and real-time personalization, and integration of foundation models and LLMs