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AI On-Device Model Update Orchestrator

Blake Goldberg

Full-time · Job · Los_Angeles

About the role

Deploying a model to the cloud takes minutes. Deploying a model to 15 million edge devices — wearables, industrial sensors, smart cameras — takes weeks and can brick hardware if it goes wrong. Our update orchestrator manages the rollout: staging updates through device cohorts, monitoring on-device performance after each wave, and automatically halting rollouts when anomalies appear. The orchestration challenge is heterogeneity. Devices have different chipsets, firmware versions, available memory, and connectivity patterns. An update that works on device type A might fail on type B because of a memory layout difference. You'll build the compatibility testing pipeline, the staged rollout logic, and the health monitoring that catches problems before they affect more than 1% of the device fleet. The edge platform team is six engineers. You'll own the update orchestration layer while others handle model compilation for different chipsets and the device management infrastructure.

Responsibilities

  • Build staged rollout orchestration for model updates across 15M devices
  • Design compatibility testing for diverse chipsets and firmware versions
  • Implement health monitoring detecting update failures in real time
  • Create automatic rollout halt when anomaly thresholds are exceeded
  • Handle connectivity-aware update scheduling for intermittently connected devices
  • Track update success rates across device cohorts and model versions

Requirements

  • 3+ years in DevOps, MLOps, or IoT platform engineering
  • Experience with staged rollout patterns and canary deployments
  • Strong Python and container orchestration skills
  • Understanding of edge computing constraints and device management
  • Comfort building systems where failures have physical consequences

Benefits

  • Fully remote with flexible schedule
  • Health, dental, and vision insurance
  • $3,000 annual learning budget
  • Stock options
  • Deploy AI to millions of devices worldwide

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Categories

MLOps & AI Infrastructure AI & Machine Learning Engineers

Skills

python docker kubernetes aws pytorch

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Job Type

Full-time

Level

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Language

English

Salary Range

$100k – $130k / year

AI Expertise

MLOps & AI Infrastructure AI & Machine Learning Engineers

Skills

python docker kubernetes aws pytorch fastapi

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