Compare · Updated 16 September 2026
Machine Learning Engineer vs Software 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 $135,980 for Software Engineer, about 47% higher. Software Engineer is faster to enter: the quickest verified route takes about 9 months versus 18 for Machine Learning Engineer. Job growth favours Machine Learning Engineer (10% projected over ten years, BLS 2025-35, versus 10%).
| Machine Learning Engineer | Software Engineer | |
|---|---|---|
| Entry median | $140,000 | $100,000 |
| Mid-career median | $200,000 | $135,980 |
| Senior median | $270,000 | $195,000 |
| Top end | $497,500 | $388,000 |
| Roadmap hours | 1,360 | 1,140 |
| Fastest way in | Data engineer to ML engineer (18 mo) | Paid bootcamp (9 mo) |
| Cheapest way in | $800 | $0 |
| Time to first job | 36–60 months | 12–24 months |
| Degree | 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. | No degree is legally required to work as a Software Engineer and no licence or mandatory certification exists, but the US Bureau of Labor Statistics reports that software developers typically need a bachelor's degree in computer and information technology or a related field, and large-employer screening still assumes one. A self-taught candidate has to replace that signal with deployed products, merged pull requests and a referral. |
| 10-year growth | 10% | 10% |
| Openings per year | 106,100 | 106,100 |
| Automation exposure | low | medium |
| Key certification | AWS Certified Machine Learning Engineer - Associate (MLA-C01, succeeded by MLA-C02) | No certification is required or expected |
| Tools | Python, PyTorch, scikit-learn, XGBoost / LightGBM, Spark | Git and GitHub, VS Code or a JetBrains IDE, PostgreSQL, Docker, Node.js or a Python web framework |
Salary figures checked September 2026 (Machine Learning Engineer) and September 2026 (Software Engineer). Sources are listed on each career page.
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.
What a Software Engineer does
A Software Engineer is a person who writes, reviews, tests and maintains the code that runs a company's product, working inside one codebase with other engineers through pull requests, automated tests and a deployment pipeline, and staying responsible for the result after it ships.
A software engineer turns requirements into working, maintained systems. In practice that means reading far more code than you write, breaking a vague ask into small changes, writing tests, getting code reviewed, shipping behind a flag, and being on the hook when it breaks. Titles vary (software developer, backend engineer, full-stack engineer, SDE) but the day-to-day is similar: a queue of tickets, a code review cycle, a deploy pipeline, and a Slack channel where things go wrong.
- Pay is high and compounds fast: the Levels.fyi US median total compensation is $195,000 and the 90th percentile is $388,000
- No licence or mandatory credential; skill is verifiable directly through code you have shipped
- Remote and hybrid work is genuinely common, which widens the employer pool beyond your city
How to choose between Machine Learning Engineer and Software Engineer
- 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.
- Pick Software Engineer if the adjacent-role side door produces more career-changer hires than any other route into Software Engineer work: taking a quality assurance, support engineering, implementation or information technology job at a software company and then transferring internally converts at a far higher rate than cold applications, because a referral and a year spent inside the codebase beat a bootcamp certificate in the 2026 junior market.
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. The natural next moves from Software Engineer are cloud and DevOps engineering, machine learning engineering, cybersecurity analysis and product management. Cloud and machine learning roles pay more and ask for deeper systems knowledge and deeper mathematics respectively; product management pays similarly, drops the coding requirement and raises the bar on written communication and stakeholder work. None of the four adds a degree requirement.
Machine Learning Engineer vs Software Engineer FAQ
Which pays more, Machine Learning Engineer or Software Engineer?
At mid-career the median is $200,000 for a Machine Learning Engineer and $135,980 for a Software Engineer; at senior level $270,000 versus $195,000. Entry medians are $140,000 and $100,000. Figures are US base plus typical bonus where reported, checked September 2026.
Is it faster to become a Machine Learning Engineer or a Software Engineer?
The quickest verified route into Machine Learning Engineer is Data engineer to ML engineer at about 18 months; for Software Engineer it is Paid bootcamp at about 9 months. Our full roadmaps run 1,360 and 1,140 study hours respectively.
Which is harder to automate, Machine Learning Engineer or Software Engineer?
We rate automation exposure low for Machine Learning Engineer and medium for Software Engineer. 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. AI coding assistants now write most of the boilerplate, which is exactly the work junior Software Engineers used to be given, and that compression is part of why the Indeed software-development postings index sat at 76.1 on 4 September 2026 against a February 2020 baseline of 100. What does not automate is reading a diff critically, integrating a change into a large existing system, debugging production at 2am and carrying the consequences, so interview loops have moved toward judgement and away from syntax recall.
Do I need a certification for Machine Learning Engineer or Software Engineer?
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. No certification is required or expected for a Software Engineer role, and nobody is hired as a developer because of one. The closest thing to real screening value is the AWS Certified Solutions Architect - Associate (SAA-C03) at $150 for the exam and 80 to 150 study hours, which helps engineers moving into cloud-heavy teams or arriving from an information technology background. Harvard's CS50x is a curriculum rather than a credential: free to audit, $219 for the verified certificate, and worth its 100 to 200 hours only for someone with no formal education signal at all.