AI Hardware Optimization Intern

tsu.edu; Houston (TX); Sur place; USD 27 552 000 - 41 328 000; Plein temps

Résumé du poste

NeuroSpark invites students to join as AI Hardware Optimization Interns, exploring performance at the interface of ML software and accelerator hardware.

The role focuses on GPU programming, computer architecture, compilers, and parallel computing, with no prior industry experience required.

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++ is required.
• Familiarity with Python is expected.
• Coursework or project experience in systems, computer architecture, OS, parallel computing, or compilers.
• Interest in mapping software performance to hardware behavior.

Responsabilités

• 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.

Connaissances

C/C++

Python

GPU programming

Parallel computing

Performance engineering

Formation

Bachelors/Masters/PhD in CS/CE/EE

Description du poste

AI Hardware Optimization Engineer Intern

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.