ML Labeling Systems Engineer

General Motors; Washington; Hybrid; USD 90,000 - 192,000; Full time

Benefits offered by this job

Hybrid work model

GM benefits (health, dental, vision, H

Retirement savings and wellness perks

Job summary

General Motors' Data Labeling Engineering team builds and operates hybrid human/machine labeling tools powering autonomous vehicle ML models. We work across software, data, and ML to create scalable training data, with a modern full‑stack including TypeScript, React, GraphQL, Python, Golang and Airflow.

This role offers end‑to‑end ownership of projects, cross‑functional collaboration with ML engineers, Ops, Product, and Data Science, and the chance to advance toward technical leadership in a

Qualifications

• 2+ years building robust web applications.
• Bachelor's degree in Computer Science or related field or equivalent work experience.
• Hands-on experience leveraging AI tools to accelerate customer value.
• Proficiency in TypeScript/React/GraphQL and backend integration.
• Experience with cloud-based applications and data/ML workflows.

Responsibilities

• Build high‑impact labeling experiences and ship scalable features.
• Develop automation and tooling to improve data quality and throughput.
• Collaborate with ML engineers to integrate ML-driven labeling workflows.
• Own projects end‑to‑end from design to rollout with code reviews.
• Collaborate across ML, Ops, Product, Data Science and platform teams.
• Champion AI‑assisted engineering and modern development workflows.

Skills

TypeScript

React

Redux

GraphQL

WebGL

AI tools

Cloud-based apps

A/B testing

Web apps

Education

Bachelor's in CS

Tools

Airflow

Job description

General Motors' Data Labeling Engineering team builds and operates hybrid human/machine labeling tools powering autonomous vehicle ML models. We work across software, data, and ML to create scalable training data, with a modern full‑stack including TypeScript, React, GraphQL, Python, Golang and Airflow.

This role offers end‑to‑end ownership of projects, cross‑functional collaboration with ML engineers, Ops, Product, and Data Science, and the chance to advance toward technical leadership in a