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AI Workload Scheduling Optimization Engineer

Ellie Petrov

Freelance · Job · Los_Angeles

About the role

GPU clusters are expensive. Idle GPUs are money on fire. Our workload scheduler decides which training jobs run on which GPUs, when to preempt low-priority work for urgent experiments, and how to bin-pack inference workloads to maximize utilization. The current scheduler uses heuristics that work at 200 jobs per day; we need an ML-based approach that works at 2,000. You'll build the prediction models that feed the scheduler: job runtime estimation, resource utilization forecasting, and priority scoring based on business impact. The scheduler itself is a constraint optimization system — your models provide the inputs that make its decisions intelligent rather than arbitrary. This is a 5-month freelance engagement, 30 hours per week. You'll work with the platform team who maintains the scheduler and the data science teams who submit the training jobs. Every scheduling improvement gets measured by cluster utilization rate and job queue wait time.

Responsibilities

  • Build prediction models for job runtime and resource utilization
  • Design priority scoring algorithms based on business impact signals
  • Integrate ML predictions into the existing constraint-based scheduler
  • Evaluate scheduling quality through utilization and wait time metrics
  • Profile GPU cluster usage patterns to identify optimization opportunities
  • Document model architecture and training procedures for handoff

Requirements

  • 3+ years in ML or systems engineering with resource optimization experience
  • Understanding of GPU scheduling and cluster management concepts
  • Strong Python skills with experience in optimization or prediction tasks
  • Familiarity with Kubernetes resource management
  • Ability to work independently within a defined engagement scope

Benefits

  • Fully remote freelance role
  • Competitive hourly rate
  • Flexible hours within US West Coast overlap
  • Access to GPU cluster for development and testing
  • Potential follow-on engagement

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Categories

MLOps & AI Infrastructure AI & Machine Learning Engineers

Skills

python kubernetes docker aws sql

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Job Type

Freelance

Level

Mid-Level

Language

English

Salary Range

$80k – $100k / year

AI Expertise

MLOps & AI Infrastructure AI & Machine Learning Engineers

Skills

python kubernetes docker aws sql statistics

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