Compare · Updated 16 September 2026
Cloud or DevOps Engineer vs Machine Learning Engineer: which pays more and which is faster?
Same sourced data as the career pages, side by side.
Machine Learning Engineer pays more at mid-career: a median of $200,000 against $134,050 for Cloud or DevOps Engineer, about 49% higher. Cloud or DevOps Engineer is faster to enter: the quickest verified route takes about 12 months versus 18 for Machine Learning Engineer. Job growth favours Machine Learning Engineer (10% projected over ten years, BLS 2025-35, versus 8%).
| Cloud / DevOps Engineer | Machine Learning Engineer | |
|---|---|---|
| Entry median | $95,000 | $140,000 |
| Mid-career median | $134,050 | $200,000 |
| Senior median | $170,500 | $270,000 |
| Top end | $300,000 | $497,500 |
| Roadmap hours | 850 | 1,360 |
| Fastest way in | Cloud support engineer at a provider or MSP (12 mo) | Data engineer to ML engineer (18 mo) |
| Cheapest way in | $300 | $800 |
| Time to first job | 12–30 months | 36–60 months |
| Degree | No degree is legally required and this is one of the few technology fields where certifications carry genuine screening weight, so AWS Certified Solutions Architect - Associate plus a public Terraform repository substitutes for one at most employers; large enterprises and federal contractors still list a bachelor's degree as a preference, and a security clearance moves you past that filter entirely. | No degree is legally required, but Machine Learning Engineer is the least degree-flexible of the data roles in practice: most large-employer postings list a bachelor’s degree in computer science or another quantitative field, and a master’s is still the standard credential for new entrants. A portfolio substitutes for it only when it contains deployed systems or open-source contributions, not notebooks. |
| 10-year growth | 8% | 10% |
| Openings per year | 23,000 | 106,100 |
| Automation exposure | medium | low |
| Key certification | AWS Certified Solutions Architect - Associate (SAA-C03) | AWS Certified Machine Learning Engineer - Associate (MLA-C01, succeeded by MLA-C02) |
| Tools | Terraform, Docker, Kubernetes, AWS (EC2, S3, VPC, IAM, RDS, EKS, Lambda), GitHub Actions | Python, PyTorch, scikit-learn, XGBoost / LightGBM, Spark |
Salary figures checked September 2026 (Cloud or DevOps Engineer) and September 2026 (Machine Learning Engineer). Sources are listed on each career page.
What a Cloud or DevOps Engineer does
A Cloud or DevOps Engineer is an engineer who builds and runs the platform other developers deploy onto: infrastructure defined in code with Terraform, containers and Kubernetes, continuous integration and delivery pipelines, observability, cloud identity and cost control, and the on-call response when any of it breaks.
A cloud or DevOps engineer builds and runs the platform everyone else deploys onto. That means infrastructure defined in code (Terraform), containers and orchestration (Docker, Kubernetes), CI/CD pipelines, observability, cost control, identity and access management, and the incident response that happens when any of it breaks at 3am. The job titles overlap heavily: cloud engineer, platform engineer, site reliability engineer, infrastructure engineer, DevOps engineer. The common thread is that your users are other engineers, and your product is the speed and safety with which they ship.
- Certifications genuinely carry weight here, unlike in application development, which gives a non-degree candidate a clear ladder
- Transfers directly from IT support, networking and military technical backgrounds
- Demand exists in every industry, not only at software companies, so you are not tied to a tech hub
What a Machine Learning Engineer does
A Machine Learning Engineer is a software engineer who owns machine learning models in production: the pipeline that assembles training data, the evaluation that decides whether a new model ships, the serving path that answers in milliseconds under load, and the monitoring that catches drift.
A machine learning engineer owns models in production. That means the pipeline that assembles training data, the training job itself, the evaluation harness that decides whether a new model is better than the old one, the serving path that answers in 50 milliseconds under load, and the monitoring that catches drift before a stakeholder does. At large companies you will specialise - ranking and recommendations, ads, search relevance, fraud and risk, speech or vision, or the platform team that builds the training infrastructure everyone else uses. At small companies you are the entire ML function, which means you also do the data engineering.
- Among the highest-paid individual contributor roles outside of finance: Levels.fyi US median total compensation of $280,000 and 90th percentile of $497,500.
- Work is intellectually dense and the feedback is objective - the model is better or it is not.
- Skills transfer cleanly to AI engineering, data engineering, backend engineering and research infrastructure.
How to choose between Cloud or DevOps Engineer and Machine Learning Engineer
- Pick Cloud or DevOps Engineer if almost nobody is hired straight into a Cloud or DevOps Engineer title with no professional experience, because the role carries production access; the route that produces most hires is an adjacent ticket-based job first - help desk, network operations centre, cloud support at a provider or managed service provider, at $45,000 to $65,000 - followed by an internal transfer in 18 to 30 months, usually with the employer paying for the certifications.
- Pick Machine Learning Engineer if almost every Machine Learning Engineer hire in the United States comes from someone already employed as a software engineer, data engineer or data scientist who moved across after two to four years, usually inside the same company, because the interview loop tests production software engineering at the same bar as a backend engineer and that skill is built by doing the job rather than by coursework.
The natural next moves are Site Reliability Engineer, security engineering, cloud architecture and back-end software engineering. Site Reliability Engineer pays similarly but demands stronger coding; cloud security pays a premium and adds compliance work; architecture roles trade the pager for design reviews and stakeholder management, with Levels.fyi solution architect reports running $167,000 at the median and $345,700 at the 90th percentile. No degree bar changes on any of these moves. The natural moves are AI Engineer, which is a faster door in because the bar sits closer to a strong application developer’s, and Data Engineer or Software Engineer, which pay less at the top but hire far more readily. Moving toward research or Applied Scientist titles raises the degree bar sharply, because those postings usually want a PhD; moving toward platform and infrastructure work does not, and pays comparably.
Cloud or DevOps Engineer vs Machine Learning Engineer FAQ
Which pays more, Cloud or DevOps Engineer or Machine Learning Engineer?
At mid-career the median is $134,050 for a Cloud or DevOps Engineer and $200,000 for a Machine Learning Engineer; at senior level $170,500 versus $270,000. Entry medians are $95,000 and $140,000. Figures are US base plus typical bonus where reported, checked September 2026.
Is it faster to become a Cloud or DevOps Engineer or a Machine Learning Engineer?
The quickest verified route into Cloud or DevOps Engineer is Cloud support engineer at a provider or MSP at about 12 months; for Machine Learning Engineer it is Data engineer to ML engineer at about 18 months. Our full roadmaps run 850 and 1,360 study hours respectively.
Which is harder to automate, Cloud or DevOps Engineer or Machine Learning Engineer?
We rate automation exposure medium for Cloud or DevOps Engineer and low for Machine Learning Engineer. Infrastructure as code and AI assistants have already eliminated manual provisioning, which used to be the entry-level work, and that is one reason the ladder into this field now starts in help desk or cloud support rather than in junior system administration. What does not automate is judgment about failure: deciding that a release is not going out, finding the cause of a 503 at 2am, and designing a system whose blast radius is small enough to survive a mistake. Code assistants now write much of the boilerplate a Machine Learning Engineer used to type, along with first-draft pipelines, tests and documentation, which is why the same loops now include an AI-assisted coding round. What does not automate is scoping the problem, designing the evaluation that decides whether a model ships, and owning a system whose failures are statistical rather than a stack trace.
Do I need a certification for Cloud or DevOps Engineer or Machine Learning Engineer?
No certification is required, but they matter more here than in any other software career because recruiters screen on them. The one with the most screening value for a first cloud role is AWS Certified Solutions Architect - Associate at $150, realistically $165 to $500 all in with a course, about 80 to 150 hours of study; Microsoft AZ-104 at $165 and Google Associate Cloud Engineer at $125 are the equivalents in those ecosystems. The Certified Kubernetes Administrator at $445, including one free retake and two Killer.sh simulator sessions, carries the most signal with experienced interviewers because it is two hours at a live terminal rather than multiple choice, and HashiCorp Terraform Associate at $70.50 is the cheapest credible addition. AWS Certified Cloud Practitioner at $100 is an orientation course, not a hiring credential. No certification is required for a Machine Learning Engineer role and no employer screens on one. The credential with real signal is the Certified Kubernetes Administrator at $445, because it is purely performance-based on live clusters, cannot be passed from question dumps, and Kubernetes is where serious training and inference workloads run - note that it expires after two years and renewal means sitting the whole exam again at full price. The AWS Certified Machine Learning Engineer - Associate at $150 and the Coursera specializations from Andrew Ng and DeepLearning.AI are curricula worth the study time, not credentials anyone hires on.