New College Grad: Engineer, Edge Systems Architecture

Work on Mobile and Client AI workloads alongside LP5X and LP6 memory workload characterization.

Originally posted 3 November 2026 by the employer.

About the role

This role focuses on memory and storage system architecture for mobile and client platforms, evaluating how workload and architectural choices influence performance, power, and system efficiency. You will work on workload characterization for mobile and client AI applications alongside LP5X and LP6 memory technologies.

What you'll do

• Work on Mobile/Client AI workloads and LP5X/LP6 Memory workload characterization, including execution, monitoring, data collection, performance analysis, and comparison of results.
• Perform benchmarking and system-level analysis across compute, Memory, and Storage to evaluate performance, power, and architectural trade-offs.
• Support engineering reporting and technical insight generation through result analysis, validation, documentation, and traceable workload data.
• Utilize AI tooling, automation, workload datasets, and execution infrastructure to support workload processing, analysis, and engineering efficiency.
• Collaborate with global engineering teams and translate requirements into MCBU workload engineering tasks.

What you'll need

• Bachelor's or Master's degree 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 system efficiency through coursework, projects, internships, or research.
• Academic exposure or understanding of CPU, GPU, NPU/AI accelerators, SoCs, DRAM, NAND, DDR/LPDDR, Memory, and Storage systems.
• Exposure to performance benchmarking, workload profiling, data collection, and analysis.
• Programming or scripting experience with Python, C/C++, or similar languages for automation and data analysis.
• Understanding of AI/ML fundamentals, AI workloads, generative AI, and AI-assisted engineering tools.

Nice to have

• 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, with hands-on exposure to benchmarking, profiling, or workload characterization.
• Exposure to hardware platforms, embedded systems, SoC development, simulation, or validation environments.
• Experience with data processing, visualization, and AI/ML workloads on CPU/GPU/NPU computing platforms.

Skills: Computer Architecture, Memory Hierarchy, LPDDR5X, Workload Characterization, Benchmarking

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