Intern - Memory Performance and Power Modeling

Originally posted 28 October 2026 by the employer.

About the role

As an intern in this role, you will work with system architects and performance engineers to analyze emerging AI workloads and evaluate computing and memory architecture. You will leverage simulation, profiling, and data analysis tools to characterize memory behavior, generate workload traces, and study system performance bottlenecks across AI training and inference applications.

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 to characterize memory access patterns, locality, reuse behavior, bandwidth utilization, and latency sensitivity.
• Develop workload traces, performance models, and architecture simulation environments.
• Investigate system bottlenecks related to memory capacity, bandwidth, latency, cache utilization, and communication overheads across CPUs, GPUs, NPUs, and accelerators.
• Create automation frameworks for workload execution, data collection, and results analysis.
• Validate simulation and analytical models using benchmark and hardware measurements.

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, or Performance Analysis.
• Programming experience in Python and C/C++.

Nice to have

• Experience with AI frameworks such as PyTorch, TensorFlow, JAX, or ONNX.
• Familiarity with LLMs, transformers, diffusion models, recommender systems, or vision models.
• Experience with workload profiling, tracing, or performance profiling tools such as Nsight, VTune, perf, rocprof, or similar tools.
• Experience with architecture simulators such as Gem5, Accel-Sim, or similar tools.
• Understanding of memory systems, including cache hierarchies and DRAM.
• Knowledge of GPU, NPU, TPU, or accelerator architectures.
• Familiarity with Linux development environments and data analysis frameworks.

Skills: workload analysis, memory traces, performance models, simulation, DRAM

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