New College Grad: Engineer, Edge Systems Architecture

Micron•Boise, ID

About The Position

The Engineer, Edge Systems Architecture role provides exposure to Memory and Storage system architecture for Mobile and Client platforms, with a focus on how workload and architectural choices affect performance, power, and system efficiency. The engineer supports engineering reporting and technical insight generation through result analysis, validation, documentation, and traceable workload data. The position involves work on Mobile/Client AI workloads and LP5X/LP6 Memory workload characterization, including workload execution and monitoring, data gathering, performance analysis, and comparison of results. The engineer contributes to benchmarking and system-level analysis across compute, Memory, and Storage, and uses AI tooling, automation, workload datasets, and execution infrastructure to support processing, analysis, reporting, and engineering efficiency. The role also involves collaboration with multifunctional and global teams, participation in technical discussions, and translating requirements and ideas into MCBU workload engineering work. Coordination across time zones may include meetings outside Boise working hours.

Requirements

Bachelor's degree, 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 system efficiency concepts 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 coursework, practical work, internship experiences, or research.
Programming or scripting experience with Python, C/C++, or similar languages, and 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 communicate findings through reports, presentations, and technical discussions.
Ability to work effectively with multifunctional and global engineering teams and dynamically take ownership as domain knowledge develops.
Ability to facilitate coordination across different 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

Gain exposure to Memory and Storage system architecture for Mobile and Client platforms and understand 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.
Participate in technical discussions with multifunctional and global collaborators, 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.