Sr. Applied Scientist, Ads AI Core Infrastructure

About the role

The Ads Real-Time Data Service team is seeking an exceptional Applied Scientist to research and develop novel approaches for agent-data interaction. This role balances applied research (60%) with productionization (40%), giving you the opportunity to both advance the state of the art and see your innovations deployed at Amazon scale.

Responsibilities

• Research and develop novel algorithms for agent-data interaction patterns that minimize latency, token consumption, and error rates
• Investigate multi-agent orchestration strategies for complex advertiser queries requiring data from multiple sources
• Develop techniques for automatic query optimization and caching strategies based on agent behavior patterns
• Invent new methods for compressing advertiser context representations while preserving semantic meaning and analytical utility
• Research optimal metadata generation techniques that help large language models understand and reason over structured advertiser data
• Design evaluations to measure the impact of different data representations on agent response quality and token efficiency
• Create feedback loops to ensure our solutions are constantly evaluating themselves and improving
• Balance applied research (60%) with productionization (40%)

Requirements

Basic Qualifications:

• 3+ years of building machine learning models for business application experience
• PhD, or Master's degree and 6+ years of applied research experience
• Experience programming in Java, C++, Python or 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, scipy etc.
• Experience with large scale distributed systems such as Hadoop, Spark etc.

Qualifications

• PhD, or Master's degree and 6+ years of applied research experience
• Experience programming in Java, C++, Python or related language
• Experience with neural deep learning methods and machine learning

Skills

• Experience with modeling tools such as R, scikit-learn, Spark MLLib, MxNet, Tensorflow, numpy, scipy etc.
• Experience with large scale distributed systems such as Hadoop, Spark etc.

Benefits

Learn more about our benefits at https://amazon.jobs/en/benefits.

Pay

Base salary range for this position is listed below. Your Amazon package will include sign-on payments and restricted stock units (RSUs).

Schedule

Final compensation will be determined based on factors including experience, qualifications, and location.