DevOps Engineer vs ML Engineer
Side-by-side comparison of DevOps Engineer and ML Engineer: salaries, skills, learning timelines, and entry threshold to help you pick a path.
At a glance
| DevOps Engineer | ML Engineer | |
|---|---|---|
| Salary comparison | $110 000 – $150 000 | $120 000 – $160 000 |
| Training Duration | 8–20 months | 9–24 months |
| Job Search Duration | 3–10 months | 4–10 months |
| English Level | B1–B2 — for reading documentation and working with international teams | B2 — for reading research papers and technical documentation |
| Education | CS or IT education is typical — practical experience matters far more than a degree | Technical degree with strong math background preferred — the math foundation is hard to build alone |
| Demand Trend | High Demand | High Demand |
Salary comparison
DevOps Engineer
United StatesSource: Habr Career, Glassdoor 2025
ML Engineer
United StatesSource: Habr Career, Glassdoor 2025
Skills compared
DevOps Engineer
Technical Skills
Soft Skills
ML Engineer
Technical Skills
Soft Skills
Key differences
- ML engineers build and deploy models. DevOps engineers handle deployment infrastructure. MLOps combines both — deploying ML models requires specialized CI/CD.
- DevOps skills are increasingly expected from ML engineers. Understanding containerization, pipelines, and cloud services is essential for production ML.
Which path should you choose?
At the mid level, DevOps Engineer and ML Engineer pay comparably — $110 000 – $150 000 and $120 000 – $160 000 respectively in the United States, according to Habr Career, Glassdoor 2025. So the choice between them usually comes down to entry threshold and timeline rather than money: DevOps Engineer typically takes 8–20 months to learn and roughly 3–10 more to land a first role, while ML Engineer takes 9–24 and 4–10 months respectively.
If getting to market and earning sooner matters most, take the path with the shorter ramp. If you're willing to invest longer for a higher long-term ceiling, lean toward the role with the wider band. The skills and key-differences sections below show how close your existing background is to each option — and that fit, more than the salary number, is usually what makes the decision hold up.
If you're still early in the switch, the faster path has a real edge: it lets you validate the career change, start earning, and build a portfolio sooner, and that compounds — every month of delay is a month of senior-level pay you postpone. If you already have transferable experience, the higher-ceiling path rewards the deeper investment. The at-a-glance table above lays out the exact trade-off in months and pay, so match it against your own timeline and savings runway.
Go deeper
DevOps Engineer
Build and automate the infrastructure that powers modern software. From CI/CD pipelines to Kubernetes clusters — DevOps engineers keep applications running reliably at scale.
ML Engineer
Machine learning engineers build the AI systems that power recommendations, search, autonomous vehicles, and language models. It is one of the highest-paid and fastest-growing roles in technology.
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