AI Training Job Priority Queue Optimizer

About the role

Responsibilities

• Build job runtime and resource prediction models from historical training data
• Design fair share allocation algorithms across teams
• Implement deadline-aware prioritization for urgent business jobs
• Create preemption logic for high-urgency job insertion
• Measure scheduling quality: cluster throughput and per-team wait time
• Build scheduling dashboards for team visibility into queue position

Requirements

• 5+ years in infrastructure or platform engineering with scheduling experience
• Understanding of resource scheduling algorithms and fairness concepts
• Strong Python and Kubernetes skills
• Experience with ML training workloads and GPU resource management
• Ability to balance competing optimization objectives

Benefits

• Fully remote with async communication
• Health, dental, and vision insurance
• $3,000 annual learning budget
• Stock options
• Optimize resource allocation for hundreds of ML researchers

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