Applied Scientist - ML for Data Center Power & Cooling

Amazon Web Services (AWS); Seattle (WA); On-site; 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 Web Services, Inc. is seeking an Applied Scientist to design and deploy ML models that optimize power and thermal infrastructure in AWS data centers.

You will work with scientists, data engineers, and software teams to model complex systems and drive cost savings, ensuring high data-center availability. You should challenge data quality, sampling biases, and model assumptions, and communicate results to senior leaders.

Qualifications

• Master's degree in a quantitative field; PhD preferred.
• Experience building machine learning models or developing algorithms for business applications.
• Experience with SQL and an RDBMS (e.g., Oracle).
• Publications on top-tier conferences (CVPR/ICCV/ECCV/NeurIPS) are a plus.

Responsibilities

• Leverage deep expertise to invent solutions for ambiguous ads measurement problems.
• Disambiguate problems to propose evaluation frameworks and success criteria.
• Write high quality technical documents and publish findings.
• Implement a significant portion of critical-path code and partner with engineers to productionize solutions.
• Collaborate with scientists to deliver impactful customer solutions.
• Share and publish works with the broader scientific community.
• Communicate clearly to scientific audiences.

Skills

Java

C++

Python

SQL

RDBMS

ML models

Algorithms

Publications

Education

Master's degree

PhD preferred

Tools

Oracle

Job description

Amazon Web Services, Inc. is seeking an Applied Scientist to design and deploy ML models that optimize power and thermal infrastructure in AWS data centers.

You will work with scientists, data engineers, and software teams to model complex systems and drive cost savings, ensuring high data-center availability. You should challenge data quality, sampling biases, and model assumptions, and communicate results to senior leaders.