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ML Platform Engineer

Joseph Kim

Full-time · Job · Los_Angeles

About the role

Our ML platform serves 8 internal teams and supports over 40 models in production. It's built on GCP with Kubernetes, MLflow, and Airflow — and it works. But as the company grows, the platform needs to evolve. We need someone to help make it self-service, scalable, and delightful for the ML engineers who use it. You'll focus on the developer experience layer: building internal CLIs, improving the model deployment workflow, adding automated testing for model artifacts, and reducing the time from experiment to production. Right now it takes 2 weeks to deploy a new model — we want that down to 2 days. This is a platform engineering role with an ML twist. You'll collaborate with ML engineers as your internal customers, understanding their pain points and building tools that make their lives easier.

Responsibilities

  • Build and maintain internal ML platform tools and CLIs
  • Reduce model deployment time from 2 weeks to 2 days
  • Automate model artifact testing and validation pipelines
  • Design self-service workflows for model registration and deployment
  • Collaborate with ML teams to understand and address platform pain points

Requirements

  • 3+ years in platform or infrastructure engineering
  • Strong Kubernetes and Docker experience
  • Familiarity with MLflow and Airflow
  • Python proficiency and experience building developer tools
  • GCP experience (GKE, Cloud Build, Artifact Registry)

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Categories

MLOps & AI Infrastructure

Skills

python kubernetes docker mlflow airflow

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Job Type

Full-time

Level

Mid-Level

Language

English

Salary Range

$100k – $130k / year

AI Expertise

MLOps & AI Infrastructure

Skills

python kubernetes docker mlflow airflow gcp fastapi

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