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AI Resource Allocation Optimization Developer

Ellie Petrov

Full-time · Job · Los_Angeles

About the role

Cloud compute is the biggest controllable cost for ML-heavy companies, and most of it is wasted on over-provisioned clusters, idle GPUs, and workloads running at the wrong time on the wrong hardware. We build the resource optimization layer that sits above Kubernetes and makes compute allocation intelligent. The core product is an allocation engine that predicts workload resource requirements, schedules jobs on the right instance types, and dynamically rightsizes running containers without disrupting the workloads. Our clients save an average of 31% on cloud compute costs, and the optimization decisions happen automatically without requiring engineers to read documentation about instance families. You'll own the demand forecasting models that power the allocation engine: predicting job duration and resource consumption from historical patterns, handling cold-start for new job types, and learning from allocation outcomes to improve future predictions. The models run inline with job submission, so latency matters — under 100ms to allocation decision.

Responsibilities

  • Build demand forecasting models for ML job duration and resource consumption
  • Design cold-start strategies for new job types without historical data
  • Implement online learning loops that improve predictions from allocation outcomes
  • Optimize inference latency to under 100ms for inline allocation decisions
  • Build evaluation pipelines comparing predicted vs actual resource consumption
  • Collaborate with scheduling engineers on how predictions feed the allocator

Requirements

  • 1+ year in ML engineering or software development with interest in infrastructure
  • Experience with resource optimization, scheduling, or capacity planning problems
  • Strong Python skills and familiarity with Kubernetes concepts
  • Understanding of online learning and feedback loop design
  • Comfort with latency-constrained ML serving

Benefits

  • Fully remote
  • Health and dental coverage
  • $3,000 learning budget
  • 401(k) with company match
  • Quarterly profit sharing

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Categories

MLOps & AI Infrastructure AI & Machine Learning Engineers

Skills

python pytorch kubernetes aws statistics

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Job Type

Full-time

Level

Junior

Language

English

Salary Range

$60k – $80k / year

AI Expertise

MLOps & AI Infrastructure AI & Machine Learning Engineers

Skills

python pytorch kubernetes aws statistics sql

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