ML Engineer. The role, the market signal, and how to build it in your org.
Trains, evaluates and deploys machine-learning models. Selects data, compares baselines and investigates model errors with the teams that use the outputs.
In a workforce of people and AI agents, this role takes shape around the workflows it supports. Define its responsibilities and handoffs, then prepare people to perform that work.
Source snapshot 2026-07-17. Postings do not confirm that a vacancy is still open.
881 postings · 491 distinct titles · from 264,613 real job postings · see the source snapshot →
What the postings ask this role to do
14,007 tasks extracted from real ML Engineer job descriptions, classified Automate / Augment / Human-only. In this source classification, 2.7% of task statements are automation candidates. The share does not measure working time or demonstrated agent performance.
- 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
Start with the work, not the org chart.
Define what this role will build, configure, decide and check. Connect that work to an enterprise-specific project and agreed assessment evidence.