Amazon Hardware Developer Engineer AWS CQC- Fab 

United States, California, Pasadena 

998894356

Key job responsibilities
Key job responsibilities & Preferred Qualifications
* Develop and maintain platforms for developing, evaluating, and deploying machine learning models for real-world applications.
* Quickly acquire knowledge of emerging technologies in Generative AI to solve complex problems in the data exploration domain.
* Work directly with scientists and engineers to understand their data and propose frameworks for ingesting it to produce actionable metrics.
* Implementation of design proposals in a logical and maintainable manner.
Example projects include:
* Develop an optical defect detection & classification ML vision model
* Establishing an active learning pipeline that improves model performance using human feedback
* Experience working in production ML environments, including deploying computer vision models into production environments.
*Experience developing automation to solve problems at scale
A day in the life
*Why AWS*
*Diverse Experiences*
Amazon 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.*Work/Life Balance*
*Inclusive Team Culture*
*Mentorship and 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.

- Experience working in production ML environments, including deploying computer vision models into production environments.
- Experience developing automation to solve problems at scale
- 2+ years of non-internship experience in system design, including design patterns, reliability and scaling aspects

- MSc or PhD in a STEM discipline
- Experience working with scientists in a research environment
- Experience with Infrastructure as Code (IaC) tools such as Terraform, Cloudformation or AWS Cloud Development Kit (CDK)
- Experience deploying and maintaining Retrieval-Augmented Generation (RAG) models with generative AI in a production environment