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AI Multi-Tenant Training Job Fairness Scheduler

David Dubois

Full-time · Job · Los_Angeles

About the role

The scheduler you'll help build decides which of dozens of enterprise customers' training jobs get GPU time first, and a badly tuned fairness policy directly translates into missed SLAs and lost revenue. Our clients include several Fortune 500 AI teams who pay a premium for guaranteed training throughput. You'll redesign our current first-come-first-served scheduler into a fairness-aware system that balances priority tiers, historical usage and job urgency, then prove out the new policy against real production workloads before it replaces the current scheduler.

Responsibilities

  • Design fairness-aware scheduling policies for multi-tenant GPU clusters
  • Benchmark new scheduling policies against production workloads
  • Partner with account teams to translate SLA commitments into scheduling rules
  • Roll out the new scheduler with a safe, gradual migration plan

Requirements

  • 6+ years in distributed systems or infrastructure engineering
  • Strong Kubernetes and GCP experience
  • Experience with scheduling or resource allocation systems
  • Track record shipping infrastructure changes to enterprise customers safely

Benefits

  • Fully remote position
  • Negotiable salary based on experience
  • Health, dental and vision coverage
  • Annual infrastructure conference budget

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Categories

MLOps & AI Infrastructure

Skills

kubernetes docker python gcp

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Job Type

Full-time

Level

Senior

Language

English

Salary Range

Negotiable / Competitive

AI Expertise

MLOps & AI Infrastructure

Skills

kubernetes docker python gcp

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