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# Applied Scientist, Amazon Redshift

- **Company:** Amazon.com, Inc.
- **Location:** East Palo Alto, CA, United States
- **Experience:** Experienced
- **Salary:** $171,600.0 - $222,200.0
- **Contract:** Permanent contract
- **Skills:** Java (Programming Language), Data Analysis, Systems Engineering, Business Software, C Sharp (Programming Language), C++ (Programming Language), Computer Programming, Data Mining, Python (Programming Language), Machine Learning, Object-Oriented Software Development, Operational Databases, Query Optimization, Software Deployment, Deep Learning, Machine Learning Operations, Amazon Redshift
- **Published:** June 20, 2026
- **Apply:** https://www.amazon.jobs/en/jobs/10454318/applied-scientist-amazon-redshift

## About the Role

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 programming in Java, C++, Python or related language
 - Experience programming with at least one modern language such as Java, C++, or C# including object-oriented design, Experience in state-of-the-art deep learning models architecture design and deep learning training and optimization and model pruning
 - Experience in solving business problems through machine learning, data mining and statistical algorithms
 - Experience in patents or publications at top-tier peer-reviewed conferences or journals

## Description

As an Applied Scientist on the Redshift Query Optimizer team, you will research and develop deep learning models that power resource prediction for one of the world's largest cloud data warehouses. You will take ownership of the end-to-end ML lifecycle, from problem formulation and data analysis to model training, evaluation, and production deployment. You will design novel approaches to understand queries and predict resource needs across diverse and evolving workloads. You will run experiments at scale on real production data, and collaborate closely with systems engineers to deliver low-latency inference in a highly available environment. You will publish your research at top-tier academic venues and contribute to the broader ML-for-systems community. And you will help shape the science roadmap for autonomous database operations while mentoring fellow scientists and engineers.


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