Amazon Applied Scientist 

United States, Washington, Seattle 

727433863

About you:
You, as the right candidate, are adept at executing every stage of the machine learning development life cycle in a business setting; from initial requirements gathering to through final model deployment, including adoption measurement and improvement. You will be working with large volumes of structured and unstructured data spread across multiple databases and can design and implement data pipelines to clean and merge these data for research and modeling.You will invent, implement, and deploy state of the art machine learning and generative AI algorithms and systems. You will build prototypes and explore conceptually large scale ML solutions. You will interact closely with our customers and with the academic community.
Key job responsibilities
What you will do- Use AWS services like SageMaker and Bedrock to deploy scalable ML models in the cloud.
Diverse Experiences
AWS values diverse experiences. Even if you do not meet all of the preferred qualifications and skills listed in the job description, we encourage candidates to apply. If your career is just starting, hasn’t followed a traditional path, or includes alternative experiences, don’t let it stop you from applying.Mentorship & Career Growth
We’re continuously raising our performance bar as we strive to become Earth’s Best Employer. That’s why you’ll find endless knowledge-sharing, mentorship and other career-advancing resources here to help you develop into a better-rounded professional.Work/Life Balance
About Sales, Marketing and Global Services (SMGS)Seattle, WA, USA

- 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 in patents or publications at top-tier peer-reviewed conferences or journals
- Experience programming in Java, C++, Python or related language
- Familiarity with LLMs and related optimization methods

- Experience in professional software development
- PhD in engineering, technology, computer science, machine learning, robotics, operations research, statistics, mathematics or equivalent quantitative field