Set up and maintain a high-end shared GPU workstation
Manage multiple user environments (Linux accounts, Docker, Python)
Assist with ML training pipelines, data preprocessing, and basic automation
Monitor per-user and total system performance (CPU, GPU, RAM, storage)
Ensure workload isolation so users don’t interfere with each other
Organize and maintain datasets, project folders, and version control
Troubleshoot hardware/software issues and update drivers/frameworks
Optionally assist in packaging simple ML models for local deployment
Requirements:
Basic Python and Linux knowledge
Familiarity with ML frameworks (PyTorch/TensorFlow)
Understanding of Docker, Git, and environment management
Interest in GPU systems, virtualization, and ML workflows
Problem-solving mindset and willingness to learn hardware/software infrastructure