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AI Cluster Scheduling Engineer

David Dubois

Full-time · Job · New_York

About the role

We operate 200 A100 GPUs across two data centers, and utilization is our business model. Every idle GPU-hour is lost revenue. Every training job that waits in queue is a frustrated client. The scheduling system that balances these two forces — maximizing utilization while minimizing wait times — is the most critical piece of our infrastructure. You'll own the job scheduling engine: priority queuing with preemption, fair-share allocation across clients, dynamic bin-packing for multi-GPU jobs, and the predictive system that forecasts demand and pre-provisions capacity. The current system works but doesn't scale past 200 GPUs — we're adding 300 more this year. This is deep systems engineering with an ML flavor. You'll need to understand GPU memory hierarchies, NVLink topologies, and the specific resource requirements of different training workloads. If you've built job schedulers for GPU clusters before, you know how rare and interesting this problem space is.

Responsibilities

  • Redesign the GPU job scheduling engine for 500+ GPU scale
  • Implement fair-share allocation and priority preemption across clients
  • Build predictive demand forecasting for proactive capacity provisioning
  • Optimize multi-GPU job placement for NVLink topology awareness
  • Own cluster utilization metrics and scheduling performance SLAs

Requirements

  • 5+ years in systems engineering with GPU cluster experience
  • Deep Kubernetes expertise for GPU workload orchestration
  • Understanding of GPU memory hierarchies and multi-GPU communication
  • Experience with job scheduling algorithms and resource allocation
  • Python proficiency for tooling and monitoring

Benefits

  • Fully remote — US East Coast hours
  • Senior compensation with equity
  • Access to cutting-edge GPU hardware
  • Solving one of the hardest problems in ML infrastructure

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Categories

MLOps & AI Infrastructure

Skills

python kubernetes docker spark aws

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Job Type

Full-time

Level

Senior

Language

English

Salary Range

$160k – $200k / year

AI Expertise

MLOps & AI Infrastructure

Skills

python kubernetes docker spark aws airflow

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