ML Engineer. The role, the market signal, and how to build it in your org.
The pre-agentic AI Builder. Still the largest single bucket, model training, evaluation, deployment. Increasingly overlaps with AI Engineer and Applied AI roles.
836 postings · 453 distinct titles · from 260,470 real job postings · see the live data →
What the postings ask this role to do
13,525 tasks extracted from real ML Engineer job descriptions, classified Automate / Augment / Human-only. Only 2.7% can be fully automated: companies are hiring this role for the judgment, not the keystrokes.
- Monitor models in production.
- Retrain models in production.
- Automate tests and deployment
- Set up alerts and dashboards.
- Containerize models with docker.
- Develop applications and systems that utilize ai tools and cloud ai services.
- Apply genai models as part of the solution.
- Construct optimized data pipelines to feed ml models.
- Maintain models in production.
- Conduct statistical analyses on business processes using machine learning techniques.
- Make team decisions.
- Engage with multiple teams and contribute to key decisions.
- Collaborate and manage the team to perform.
- Engage with multiple teams and contribute on key decisions.
- Govern models from a risk perspective.
From the market's version of this role to your version of it
Compose your org's ML Engineer job description
Start from the tasks real postings ask for, keep the ones that match your operation, add what is specific to you. The tasks carry their AI classification, so the JD you take away already says what AI runs and what stays with people.
Start with the work, not the org chart.
Run the audit on one operation and see what this role would own first.