AI Hardware Optimization Engineer Intern

Santa Clara, CAOnsiteInternshipJunior / Entry-levelPosted 2 hours ago

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Required skills

GPU programming
computer architecture
compilers
parallel computing
performance engineering
C/C++
Python
ML operators
model workloads
kernel execution

Nice to have

CUDA
kernels
HPC
ML systems
PyTorch
JAX

Role summary

NeuroSpark is seeking an AI Hardware Optimization Intern to work on performance challenges at the intersection of machine learning software and accelerator hardware. This role involves benchmarking ML workloads, profiling kernel execution, developing optimizations, and experimenting with various techniques across different hardware platforms. The intern will gain experience in GPU programming, computer architecture, compilers, parallel computing, and performance engineering, working with engineers on production-relevant AI tasks. Prior industry experience is not required.

About the Role

As an AI Hardware Optimization Intern at NeuroSpark, you will work on performance problems at the boundary between machine learning software and modern accelerator hardware. This internship is designed for students interested in GPU programming, computer architecture, compilers, parallel computing, or performance engineering. Prior industry experience is not required.

What You’ll Do

Benchmark ML operators and model workloads on accelerators.
Profile kernel execution, memory access, and hardware utilization.
Help develop and test kernel-level and runtime-level optimizations.
Experiment with tiling, operator fusion, memory reuse, and parallel execution.
Build performance benchmarks and profiling tools.
Compare optimization strategies across different hardware platforms.
Work directly with engineers on production-relevant AI workloads.

Minimum Qualifications

Currently pursuing a Bachelor’s, Master’s, or PhD in Computer Science, Computer Engineering, Electrical Engineering, or a related field.
Programming experience in C/C++.
Familiarity with Python.
Coursework or project experience in systems, computer architecture, operating systems, parallel computing, or compilers.
Interest in understanding how software performance maps to hardware behavior.

Preferred Qualifications

CUDA or other GPU programming experience.
Projects involving kernels, compilers, HPC, or ML systems.
Familiarity with PyTorch, JAX, TensorFlow, or Triton.
Experience using performance profilers.
Understanding of GPU memory hierarchy and parallel programming concepts.

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