Applied Scientist - ML for Data Center Power Optimization

Amazon Science; Herndon (VA); On-site; USD 136,000 - 184,000; Full time

Job summary

Amazon Science in Virginia's Herndon area is seeking an Applied Scientist to advance ML models that optimize data center power and thermal infrastructure. You will collaborate with scientists, data engineers and software teams to model and improve power systems.

You will rigorously question data sources, push boundaries of sampling and model assumptions, and raise the bar on code quality. The role values curiosity, cross-functional communication, and the ability to translate business questions

Qualifications

• Master's degree in a quantitative field or related area.
• Experience building machine learning models or developing algorithms for business applications.
• Publications in top-tier conferences (e.g., CVPR, ICCV, ECCV, NeurIPS) are a plus.

Responsibilities

• Leverage deep expertise to invent solutions to ambiguous ads measurement problems.
• Disambiguate problems to propose clear evaluation frameworks and success criteria.
• Write high quality technical documents and publish works with the scientific community.
• Implement a significant portion of critical-path code and collaborate with engineers to put solutions into production.
• Collaborate with scientists to deliver impactful customer solutions.

Skills

Java

C++

Python

SQL

RDBMS

Data Warehouse

ML models

Algorithms

Publications

Education

Master's degree

PhD

Job description

Amazon Science in Virginia's Herndon area is seeking an Applied Scientist to advance ML models that optimize data center power and thermal infrastructure. You will collaborate with scientists, data engineers and software teams to model and improve power systems.

You will rigorously question data sources, push boundaries of sampling and model assumptions, and raise the bar on code quality. The role values curiosity, cross-functional communication, and the ability to translate business questions