Applied Scientist, AWS Infrastructure Services Science
About the role
AWS Infrastructure Services Science (AISS) researches and builds machine learning models that influence power utilization at data centers to ensure the health of thermal and electrical infrastructure at high utilization. As an Applied Scientist, you will work on the Science team and partner closely with scientists, data engineers, and software teams to model and optimize power infrastructure. Your model outputs will directly influence data center topology and drive exceptional cost savings.
You will research and deploy machine learning models that optimize power and thermal infrastructure, solve data mapping and quality issues across AWS, and contribute to the Science team vision. You maintain rigorous standards, question data sources for biases and accuracy, challenge model assumptions, and take ownership of data pipelines and code. You have a passion for excellence, limitless curiosity, strong business and communication skills, and are comfortable learning data center architecture and electrical engineering concepts to build models. You thrive in an agile, fast‑paced environment, handle ambiguity, juggle competing priorities, and help drive senior‑level conversations about cost‑savings versus customer availability.
Responsibilities
- Leverage deep expertise in one or more scientific disciplines to invent solutions to ambiguous ads measurement problems
- Disambiguate problems to propose clear evaluation frameworks and success criteria
- Work autonomously and write high quality technical documents
- Implement a significant portion of critical‑path code, and partner with engineers to directly carry solutions into production
- Work closely with other scientists to deliver impactful customer solutions
- Share and publish works with the broader scientific community through meetings and conferences
- Communicate clearly to scientific audiences
Requirements
- Master's degree
- Experience programming in Java, C++, Python or related language
- Experience with SQL and an RDBMS (e.g., Oracle) or Data Warehouse
- Experience building machine learning models or developing algorithms for business application
- Experience implementing algorithms using toolkits and self‑developed code
- Publications on top‑tier conferences such as CVPR, ICCV, ECCV or NeurIPS
Qualifications
- Experience researching machine learning, deep learning, NLP, computer vision, or data science
- PhD in computer science, mathematics, statistics, machine learning, or equivalent quantitative field