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. This role focuses on memory trace-based workload analysis to help drive architectural decisions for future memory technologies and AI platforms.
What you'll do#
- Analyze AI/ML workloads including Large Language Models (LLMs), recommendation models, computer vision, multimodal, and generative AI applications.
- Collect, process, and analyze memory traces from AI training and inference workloads.
- Characterize memory access patterns, locality, reuse behavior, bandwidth utilization, and latency sensitivity.
- Characterize compute, memory, storage, and interconnect behavior across AI training and inference workloads.
- Develop workload traces, performance models, and architecture simulation environments.
- Investigate system bottlenecks related to memory capacity, bandwidth, latency, cache utilization, and communication overheads.
- Evaluate architectural tradeoffs across CPUs, GPUs, NPUs, accelerators, and memory technologies.
- Create automation frameworks for workload execution, data collection, and results analysis.
- Validate simulation and analytical models using benchmark and hardware measurements.
- Present findings through technical reports, visualizations, and architecture reviews.
What you'll need#
- Currently 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, and performance analysis.
- Programming experience in Python and C/C++.
- Strong analytical, problem-solving, and quantitative reasoning skills.
- Excellent written and verbal communication skills.
- Familiarity with AI frameworks such as PyTorch, TensorFlow, JAX, or ONNX.
- Experience with workload profiling, tracing, or performance analysis tools like Nsight, VTune, perf, or rocprof.
- Knowledge of GPU, NPU, TPU, or accelerator architectures and memory systems including cache hierarchies and DRAM.
- Experience with architecture simulators such as Gem5 or Accel-Sim.
- Familiarity with Linux development environments and data analysis frameworks.
Location & details#
- This is a full-time, paid internship.
- Work modality: On-site.
- Locations: San Jose, California, or Austin, Texas.
- This position does not offer sponsorship.
About Micron Technology
Micron Technology produces memory and storage solutions including DRAM, NAND, and NOR products. The company operates as a public organization with over 43,000 employees. Founded in 1978, it maintains its headquarters in Boise, Idaho. Micron serves various markets ranging from data centers to mobile devices.
How to get in at Micron Technology
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