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AI Observability Engineer

Caleb Rodriguez

Full-time · Job · Los_Angeles

About the role

When a traditional API returns a 500 error, you know something broke. When an AI model starts giving subtly wrong answers, nobody notices until the damage is done. Model observability — catching quality degradation before users complain — is the hardest unsolved problem in production ML. We build observability tools for AI-native companies. Our platform monitors model predictions in real-time, detects distribution drift, tracks feature importance changes, and alerts engineering teams when quality drops below configured thresholds. Think Datadog for ML models. You'll work on the core detection engine: statistical methods for drift detection, anomaly scoring for prediction distributions, and automated root cause analysis that tells an engineer not just that something changed, but what changed and why.

Responsibilities

  • Build real-time model observability and drift detection systems
  • Design statistical methods for detecting quality degradation in production models
  • Implement automated root cause analysis for model performance changes
  • Own the alerting and notification pipeline for ML quality incidents
  • Architect the observability platform for horizontal scaling across clients

Requirements

  • 5+ years in observability, monitoring, or production ML engineering
  • Strong statistical foundation for drift and anomaly detection
  • Kubernetes and GCP experience for platform infrastructure
  • Python proficiency with FastAPI for service development
  • Experience with MLflow or similar model management tools

Benefits

  • Fully remote — US time zones
  • Senior compensation with equity
  • $3,500 annual conference and learning budget
  • Solving one of the hardest problems in production ML

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Job Type

Full-time

Level

Senior

Language

English

Salary Range

$130k – $160k / year

AI Expertise

MLOps & AI Infrastructure

Skills

python kubernetes docker gcp mlflow fastapi sql

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