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AI Infrastructure Engineer

David Dubois

Full-time · Job · New_York

About the role

We run a private GPU cluster — 200 A100s across two data centers on the East Coast. Our clients rent compute for model training, and our infrastructure team keeps everything running at 98%+ utilization. When a training job fails at 3am, we need to know why before the client does. You'll join the infrastructure team (4 engineers) and focus on job scheduling, resource allocation, and cluster monitoring. We use Kubernetes for orchestration, Spark for distributed preprocessing, and a custom Airflow-based job management system that handles priority queuing and preemption. This is an on-site role at our data center in New Jersey. You'll have hands-on access to the hardware, which matters when you're debugging GPU memory errors or network topology issues. If you like infrastructure problems with physical consequences, this is your role.

Responsibilities

  • Manage and optimize GPU cluster scheduling and resource allocation
  • Build monitoring and alerting systems for cluster health and utilization
  • Own the Airflow-based job management and priority queuing system
  • Debug hardware-level issues (GPU memory, NVLink, networking)
  • Collaborate with clients' ML teams on training job optimization

Requirements

  • 3+ years in infrastructure or systems engineering
  • Strong Kubernetes experience in bare-metal or hybrid environments
  • Familiarity with GPU computing and CUDA debugging
  • Experience with Airflow and distributed systems
  • Comfort with on-site work and occasional off-hours incident response

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Categories

MLOps & AI Infrastructure

Skills

python kubernetes docker aws spark

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Job Type

Full-time

Level

Mid-Level

Language

English

Salary Range

$100k – $130k / year

AI Expertise

MLOps & AI Infrastructure

Skills

python kubernetes docker aws spark airflow

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