Artificial intelligence-assisted Reliability Engineer Intern , Amazon Development Center

Job Description:

Amazon development center develops innovative consumer-centric safety total solutions and products mainly to make neighborhoods safer. As a smart security company, we strive to make safety and security accessible to everyone, empowering communities to work together for a safer future.
As an AI-assisted reliability engineer intern, you will be part of a reliability team to assist in delivering solutions through artificial intelligence assistance. You will utilize Amazon-established AI tools (so-called AI agent, AI proxy) to assist senior reliability engineers in both the US and Taiwan for the development of reliability execution methodologies, failure analysis techniques, product lifespan projection to enhance product reliability and design optimization. The selected candidate will be responsive, flexible, and able to succeed within an open-collaborative peer environment.

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Key job responsibilities
The Role:
* 1-year-intern. Able to work 4~5 days per week.
* Develop and implement novel machine learning algorithms, focusing on computer vision and generative AI applications.
* Design and optimize scalable AI systems for large-scale datasets, ensuring high performance and accuracy
* Apply state-of-the-art Machine Learning and AI research to solve complex reliability challenges and product lifespan projection.
* Create clear technical documentation and reports to effectively communicate concepts and results.
* Work closely with senior reliability engineers in reporting reliability execution progress, and failure analysis.
* Participate in evaluating and developing reliability test methodologies to reduce test time and increase test coverage.
* Working location: Taipei

Candidate Requirements:

- Understanding of the principles and basic structures of measuring instruments such as heat soak chambers, FTIR, and SEM, etc.
- Strong organizational and problem-solving skills
- Demonstrated critical thinking capability
- Self-motivated and proactive
- Experience or coursework with reliability knowledge, statistical knowledge and be able to use reliability and statistical methods to do data analysis.
- Experience with failure analysis techniques to isolate failure for test issues.

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