Micron is a global leader in memory and storage, powering AI and compute-intensive applications from cloud to edge with innovative semiconductor solutions.
About the role
As an AI/ML Workload Analysis Intern , you will work with system architects and performance engineers to analyze emerging AI workloads and evaluate next-generation computing and memory architecture. You will develop methodologies to characterize memory behavior, generate workload traces, and uncover performance bottlenecks across AI training and inference applications. Found on 1752vc Careers, the job board for startup and VC roles.
What they're looking for
- Pursuing an MS or PhD in Computer Engineering, Computer Science, Electrical Engineering, Data Science, or a related field
- Coursework or research experience in: Computer Architecture
- Machine Learning / Deep Learning
- Parallel Computing
- Operating Systems
- Data Structures and Algorithms
More about this role
Our vision is to transform how the world uses information to enrich life for all .
Micron Technology is a world leader in innovating memory and storage solutions that accelerate the transformation of information into intelligence, inspiring the world to learn, communicate and advance faster than ever.
As an AI/ML Workload Analysis Intern , you will work with system architects and performance engineers to analyze emerging AI workloads and evaluate next-generation computing and memory architecture. You will develop methodologies to characterize memory behavior, generate workload traces, and uncover performance bottlenecks across AI training and inference applications. You will develop insights into how large language models (LLMs), recommendation systems, vision models, and multimodal AI applications interact with CPUs, GPUs, NPUs, memory subsystems, and interconnects.
You will leverage simulation, profiling, and data analysis tools to study performance bottlenecks, memory behavior, bandwidth requirements, and scalability characteristics of AI workloads. The focus of this role is on memory trace-based workload analysis , helping drive architectural decisions for future memory...
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