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AI Edge Inference Optimization Engineer

Covex Cloud

Remote · $160k – $200k / year · Contract · Posted 3 months ago

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About the role

PyTorch models, Kubernetes clusters, and a serverless inference layer are what our platform runs on today, but edge deployments are where our biggest clients want to go next. If you know how to squeeze inference latency down on constrained hardware without gutting model accuracy, this role is built for you. You'll take existing cloud-trained models and optimize them for edge inference, working with a small, fast-moving infrastructure team that ships weekly. No layers of approval, just a clear problem and the tools to solve it.

Fully remote.

Responsibilities

  • Optimize existing models for low-latency edge inference
  • Benchmark accuracy and latency tradeoffs across hardware targets
  • Build deployment pipelines for containerized edge inference
  • Work with GCP and Kubernetes to manage hybrid cloud-edge deployments
  • Document optimization techniques for the rest of the engineering team

Requirements

  • 4+ years of experience with PyTorch model optimization
  • Experience with Docker and Kubernetes in production
  • Familiarity with edge or embedded inference constraints
  • Comfortable working with GCP infrastructure
  • Track record of shipping performance-critical ML systems

Benefits

  • Fully remote, contract engagement with potential to extend
  • Competitive contract rate
  • Flexible hours across time zones
  • Direct access to senior infrastructure engineers
  • Possibility of conversion to full-time

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Categories

MLOps & AI Infrastructure

Skills

python pytorch docker kubernetes gcp

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Job Type

Contract

Level

Senior

Language

English

Salary Range

$160k – $200k / year

AI Expertise

MLOps & AI Infrastructure

Skills

python pytorch docker kubernetes gcp

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