Profile cover
D

AI Training Job Priority Queue Optimizer

Mivex Cloud

Remote · $100k – $130k / year · Full-time · Posted 5 months ago

No longer accepting applications

This role is no longer accepting applications

Don't miss the next role like this

Create a free profile with your skills. We match you to new AI roles as they open, and companies hiring can contact you directly.

Create free profile, 2 minutes

Similar open roles you can apply to now:

See all open AI jobs

About the role

A GPU cluster shared across 30 data science teams is a recipe for conflict. The PhD researcher running a 3-day training job blocks the engineer who just needs 10 minutes to verify a bug fix. The team with the business deadline always pushes to the front regardless of actual priority. Our priority queue optimizer replaces politics with algorithms, ensuring cluster time is allocated based on job urgency, resource efficiency, and fair usage policies. You'll build the scheduling intelligence: predicting job runtime and resource requirements before submission, computing fair share allocations across teams, prioritizing urgent jobs from approaching deadlines, and dynamically preempting low-priority jobs when high-urgency work arrives. Every scheduling decision gets measured against two objectives: total cluster throughput and user-experienced wait time. The cluster operations team is four engineers. You'll own the scheduling logic while others handle the Kubernetes job submission and resource monitoring infrastructure.

Fully remote.

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

Keep exploring related roles

Use these topic pages to discover more roles in the same hiring cluster.

Categories

MLOps & AI Infrastructure Data Science & Analytics

Skills

python kubernetes docker aws sql

Related remote AI jobs

Nearby roles that share similar skills, categories, or hiring intent.

Browse all jobs

Solutions Architect - Digital Native Business (Geo)

Databricks

Full-time

Senior Solutions Engineer, Observe

Snowflake

Full-time Senior

Senior Release Manager

Agility Robotics

Full-time Senior

Senior Data Analyst: Multiple Teams

Constructor

Full-time Senior

Staff Data Scientist

Gradientai

Full-time Staff

Senior Analytics Engineer

Constructor

Full-time Senior

Job Type

Full-time

Level

Senior

Language

English

Salary Range

$100k – $130k / year

AI Expertise

MLOps & AI Infrastructure Data Science & Analytics

Skills

python kubernetes docker aws sql statistics

Ready to apply for this role?

Create a free talent account.

  • Find AI roles in one place
  • Get AI-matched job recommendations
  • Reply to companies that message you
  • Build your public AI talent profile
Create free profile and apply Log in

This role is closed. Create your free profile to apply to similar open roles.

Don't miss the next one: get matched to new roles like this.

Create free profile