Inference Optimization Intern – Performance Modeling

Key Responsibilities

This intensive internship offers a unique opportunity to contribute to the development of a simulator and profiling framework for foundation model inference on NVidia GPUs.

Responsibilities include

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• Develop analytical performance models for GPU kernels and inference workloads.
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• Build and validate a simulator to estimate theoretical hardware performance limits.
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• Compare measured kernel performance against architectural peak throughput.
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• Identify performance bottlenecks in compute, memory, communication, and scheduling.
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• Analyze GPU execution using NVIDIA Nsight Systems and Nsight Compute.
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• Investigate PTX and SASS code generation to understand low-level execution behavior.
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• Collaborate with researchers and engineers to optimize inference kernels for transformer-based models.
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• Evaluate utilization of Tensor Cores, memory bandwidth, caches, and instruction pipelines.
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• Design profiling methodologies for Hopper and Blackwell architectures.
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• Document findings and provide actionable recommendations for performance improvements.

Academic Qualifications

Currently pursuing a degree in Computer Science, Computer Engineering, Electrical Engineering, Artificial Intelligence, High-Performance Computing, or a related quantitative discipline.

Preferred Qualifications

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• Experience with CUDA programming and GPU kernel development.
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• Understanding of NVIDIA GPU architecture and memory hierarchy.
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• Familiarity with performance profiling tools such as Nsight Systems and Nsight Compute.
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• Knowledge of PTX, SASS, and low-level GPU execution.
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• Experience optimizing CUDA kernels for throughput and latency.
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• Understanding of roofline analysis, performance modeling, and hardware utilization metrics.
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• Experience with deep learning frameworks such as PyTorch or TensorFlow.
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• Strong programming skills in C++, CUDA, and Python.

Desired Skills

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• Performance engineering mindset.
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• Strong analytical and debugging abilities.
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• Interest in AI systems, inference optimization, and hardware-software co-design.
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• Ability to work independently on research and engineering challenges.
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• Excellent written and verbal communication skills.