Applied Scientist II — Machine Learning for Search Ranking

Amazon; Seattle (WA); On-site; USD 143,000 - 193,000; 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 a highly skilled ML/DS professional to advance search relevance for Amazon’s product search. You will design and deploy high-performance ML models, analyze large-scale data, and run offline and online experiments to improve ranking across millions of products.

The role emphasizes collaboration with cross-functional teams, publication of results at scientific venues, and contributions to both research and production pipelines.

Qualifications

• PhD or Master’s with 4+ years of CS/CE/ML or related field experience.
• Proficiency in Java, C++, Python or related languages.
• Experience in algorithms, data structures, parsing, numerical optimization, data mining, parallel/distributed computing, or HPC.

Responsibilities

• Analyze the data and metrics from traffic into Amazon's product search service.
• Design, build, and deploy ML solutions to improve search ranking.
• Evaluate proposed solutions via offline benchmarks and online A/B tests in production.
• Publish and present work at internal and external ML/NLP/IR venues.

Skills

Java

C++

Python

Algorithms and data structures

Numerical optimization

Data mining

Parallel and distributed computing

High-performance computing

Education

PhD in CS/CE/ML

Master’s degree in CS/CE/ML + 4+ years experience

Tools

MXNet

TensorFlow

Caffe

PyTorch

Unix/Linux

Job description

Amazon.com Services LLC is seeking a highly skilled ML/DS professional to advance search relevance for Amazon’s product search. You will design and deploy high-performance ML models, analyze large-scale data, and run offline and online experiments to improve ranking across millions of products.

The role emphasizes collaboration with cross-functional teams, publication of results at scientific venues, and contributions to both research and production pipelines.