Responsibilities:
- 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
Who Should Apply:
- CS/IT freshers, early-career developers, or anyone curious about ML-Ops, AI workflows, and managing GPU-powered infrastructure. Ideal for those who want hands-on experience with real-world ML/video workloads.
Perks:
- Unpaid internship
- Hands-on experience managing high-end GPU workstations and ML pipelines
- Exposure to a collaborative startup environment
- Letter of Recommendation (LOR) for outstanding contributions
- Work on live AI, ML, and EdTech projects
- Self-Learn about remote workstation access, Docker/virtualization, and multi-user GPU setups
Requirements:
- Basic Python and Linux skills
- Familiarity with ML frameworks (PyTorch, TensorFlow)
- Understanding of Docker, Git/GitHub, and environment management
- Exposure to GPU systems, virtualization, or high-performance computing is a plus
- Interest in monitoring and optimizing per-user and total system performance
- Basic knowledge of data handling, automation, and scripting
