AI Systems Engineer: Multi-GPU HPC & ML Pipelines

California State University, Chico; San Luis Obispo (CA); On-site; USD 111,000 - 127,000; Full time

Job summary

Cal Poly’s Information Technology Services is seeking a Software Engineer to support AI Factory and Center of Excellence, enabling HPC and AI infrastructure anchored by the NVIDIA DGX-B200 system. You will develop and scale software pipelines for large-scale ML training, fine-tuning, and deployment, bridging GPU-oriented engineering with research-focused software support.

Ideal candidates will optimize workloads across multi-GPU environments, contribute to reproducible research workflows, and

Qualifications

• Thorough and advanced knowledge of Python with experience in performance-oriented languages (e.g., C++ or CUDA), applied to AI/ML software engineering.
• Advanced skills in distributed computing, multi-GPU parallel processing, and performance optimization, including GPU system software such as drivers, CUDA toolkits, and container runtimes.
• Demonstrated experience with AI/ML frameworks such as PyTorch, TensorFlow, or JAX, and the ability to develop reproducible research workflows.
• Advanced knowledge of containerization (Docker) and orchestration (Kubernetes, Slurm, Ray) to improve research productivity and reproducibility.
• Thorough and advanced knowledge of Linux-based systems, with the ability to work with technical and non-technical staff to translate user requirements into technology-based solutions.

Skills

Python

C++

CUDA

Distributed computing

Multi-GPU

PyTorch

TensorFlow

JAX

Docker

Kubernetes

Slurm

Ray

Linux

Education

Bachelor’s degree or equivalent in related field

Tools

Docker

Kubernetes

Slurm

Ray

CUDA Toolkit

Job description

Cal Poly’s Information Technology Services is seeking a Software Engineer to support AI Factory and Center of Excellence, enabling HPC and AI infrastructure anchored by the NVIDIA DGX-B200 system. You will develop and scale software pipelines for large-scale ML training, fine-tuning, and deployment, bridging GPU-oriented engineering with research-focused software support.

Ideal candidates will optimize workloads across multi-GPU environments, contribute to reproducible research workflows, and