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AI Defect Detection Pipeline Engineer

Alexander Lindqvist

Full-time · Job · Chicago

About the role

Factory floors generate images at a pace that would overwhelm any human inspector — 200 cameras capturing 4,000 images per minute across three manufacturing lines. Our computer vision system processes every frame, detects defects as small as 0.1mm, and triggers real-time alerts before defective parts move downstream. The models work. The problem is keeping them working. Manufacturing environments are hostile to ML models. Lighting changes with the weather, camera lenses fog up, product variants shift quarterly, and a new defect type appears every few weeks. You'll build the pipeline infrastructure that keeps our detection models accurate despite these shifts: automated retraining triggers, data quality monitoring, model versioning, and drift detection — all running 24/7 with zero tolerance for downtime. You'll split time between our Chicago office (for factory visits and hardware integration) and remote work. The CV team is six engineers; you'll focus on the operational ML infrastructure while others handle model architecture.

Responsibilities

  • Build automated retraining pipelines triggered by performance degradation
  • Implement data quality monitoring for factory camera feeds
  • Design model versioning and rollback systems for production deployments
  • Create drift detection alerts for changing manufacturing conditions
  • Optimize inference pipeline for real-time processing at 4,000 images per minute
  • Coordinate with factory operations teams during model updates

Requirements

  • 3+ years in ML engineering with computer vision deployment experience
  • Experience building MLOps pipelines: training, evaluation, deployment, monitoring
  • Proficiency with Docker, MLflow, and cloud services
  • Understanding of manufacturing or industrial automation environments
  • Comfort with on-site visits to factory environments

Benefits

  • Hybrid — 2 days Chicago office/factory, 3 days remote
  • Health, dental, and vision insurance
  • 401(k) with match
  • PPE and safety training provided for factory visits
  • $2,500 professional development budget

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Categories

AI & Machine Learning Engineers MLOps & AI Infrastructure

Skills

python pytorch computer vision docker mlflow

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Job Type

Full-time

Level

Mid-Level

Language

English

Salary Range

$80k – $100k / year

AI Expertise

AI & Machine Learning Engineers MLOps & AI Infrastructure

Skills

python pytorch computer vision docker mlflow aws

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