Intern - SSD Systems Design Engineer
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About this role
Department Intro
The Enterprise SSD Systems Design Engineering team develops enterprise‑class solid‑state storage solutions that support Micron’s global leadership in memory and storage innovation. The team collaborates across hardware, firmware, validation, reliability, and manufacturing groups to enable cutting‑edge SSD technologies for data‑center and AI‑driven applications.
Position Overview
The SSD Systems Design Engineering Intern supports enterprise SSD product development by assisting with system‑level characterization, data analysis, and engineering investigations. The role includes ownership of a summer project focused on organizing, managing, and enabling AI‑driven insights from characterization data used throughout SSD research, development, and system‑level validation.
Responsibilities
- Support enterprise SSD product development activities from a systems engineering perspective.
- Assist with system‑level characterization, data analysis, and engineering investigations for SSD R&D.
- Design and execute a project centered on improving data organization, management, and analysis.
- Explore AI‑enabled or data‑driven approaches to enhance engineering workflows and data usability.
- Collaborate with cross‑functional engineering teams and present technical findings to the Systems Design Engineering team.
Minimum Qualifications
- Currently enrolled in a Bachelor’s program in Electrical Engineering, Computer Engineering, Computer Science, or a closely related technical field.
- Completion of foundational coursework in digital systems, computer architecture, programming/scripting (e.g., Python, C/C++), data analysis, algorithms, or statistics.
- Strong analytical, problem‑solving, and communication skills.
- Ability to work effectively both independently and within a team environment.
Preferred Qualifications
- Interest in storage systems, system‑level engineering, or hardware/software interaction.
- Exposure to data analysis tools, databases, or introductory machine learning concepts.
- Experience working with lab data, test results, or engineering datasets through coursework or projects.
- Familiarity with Linux environments or engineering development workflows.