New College Grad: Engineer, Edge Systems Architecture

Micron Technology•Boise, ID

About The Position

Micron Technology is hiring a new college graduate to support Edge Systems Architecture work focused on Mobile and Client platforms. The role develops exposure to memory and storage system architecture and how workloads and architectural choices affect performance, power, and system efficiency. It supports workload characterization and benchmarking across compute, memory, and storage, including Mobile/Client AI workloads and LP5X/LP6 memory workloads. The engineer will execute and monitor workloads, gather and analyze data, compare results, and contribute to technical reporting, validation, and documentation. The role works with global, cross-functional collaborators to translate requirements and ideas into MCBU workload engineering activities, including coordination across time zones. It also uses AI tooling, automation, workload datasets, and execution infrastructure to support workload processing, analysis, reporting, and engineering efficiency.

Requirements

Bachelor's degree or Master's degree, or equivalent experience, in Electrical Engineering, Computer Engineering, Computer Science, or a related technical subject area.
Foundational understanding of computer architecture, memory hierarchy, operating systems, and concepts related to overall system efficiency, acquired through coursework, projects, internships, or research.
Understanding or academic exposure to CPU, GPU, NPU/AI accelerators, SoCs, DRAM, NAND, DDR/LPDDR, Memory, and Storage systems.
Exposure to performance benchmarking, workload profiling, data collection, and analysis through classes, practical work, internships, or research.
Programming or scripting experience with Python, C/C++, or similar languages, and an interest in automation and engineering data analysis.
Understanding of AI/ML fundamentals, AI workloads, generative AI, and AI-assisted engineering tools and their application to workload and system analysis.
Strong analytical and problem-solving fundamentals, with the ability to learn new architectures, tools, and workload methodologies.
Ability to analyze technical data, document observations, and clearly communicate findings through reports, presentations, and technical discussions.
Ability to work effectively with multi-functional and global engineering teams and take ownership as domain knowledge develops.
Ability to facilitate coordination across time zones, which may include meetings outside Boise working hours.

Nice To Haves

Academic, project, internship, or research exposure to Mobile, Client, AI, Memory, Storage, or system performance engineering.
Familiarity with DRAM, NAND, DDR/LPDDR5/5X, PCIe, or NVMe concepts.
Hands-on exposure to benchmarking, profiling, workload characterization, or performance data analysis.
Exposure to AI/ML workloads, CPU/GPU/NPU computing, generative AI, or AI-assisted engineering tools.
Academic or internship experience with Python/C/C++, scripting, automation, data processing, or visualization.
Exposure to hardware platforms, embedded systems, SoC development, simulation, or validation environments.
Interest in developing deeper expertise in Memory/Storage architecture, workload engineering, AI workloads, and system-level performance analysis.

Responsibilities

Build understanding of Memory and Storage system architecture for Mobile and Client platforms and how workload and architectural choices influence performance, power, and system efficiency.
Support engineering reporting and technical insight generation through result analysis, validation, documentation, and consistent, traceable workload data.
Engage with multi-functional and global collaborators, participate in technical discussions, and help translate requirements and ideas into MCBU workload engineering work.
Support global collaboration across time zones and represent MCBU workload engineering in collaborator discussions.
Work on Mobile/Client AI workloads and LP5X/LP6 Memory workload characterization, including workload execution, monitoring, data gathering, performance analysis, and comparison of results.
Contribute to benchmarking and system-level analysis across compute, Memory, and Storage to evaluate performance, power, and architectural trade-offs.
Use AI tooling, automation, workload datasets, and execution infrastructure to support workload processing, analysis, reporting, and engineering efficiency.