Job Description
A role focused on developing a dynamic resource allocation system to optimize cloud infrastructure efficiency and productivity, primarily by provisioning GPU virtual machines and integrating with Kubernetes and internal scheduling tools.
Key Responsibilities
- Develop a system to provide users with GPU VMs for their development environment
- Create a dynamic VM allocation mechanism integrated into a shared GKE resource pool
- Integrate VM provisioning and lifecycle management into the in-house ML Scheduler
Requirements
- Currently pursuing a Bachelor’s or Master’s degree in Computer Science or related field and graduating before December 2026
- Proficient in Machine Learning concepts and applications
- Familiarity with Google Kubernetes Engine (GKE) and cloud resource management
- Develop a system to provide users with GPU VMs for their development environment
- Create a dynamic VM allocation mechanism integrated into a shared Google Kubernetes Engine (GKE) resource pool
- Integrate the VM provisioning and lifecycle management system into the in-house ML Scheduler
Benefits & Perks
Ready to Apply?
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