What does a machine learning engineer do, and which skills do you need?
A machine learning engineer frames a business or technical problem as a model a computer can solve, judges whether the project is feasible, and works with users and managers on what the system must do. Employers test problem framing and the reasoning behind a model as much as coding, so show the decisions behind your projects.
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What does a machine learning engineer do?
O*NET has no machine learning engineer occupation. The closest O*NET occupation is Computer and Information Research Scientists (O*NET 15-1221.00), and its core tasks include:
Read each job description for production duties, such as deploying and monitoring models in a product, because the O*NET list does not cover them.
- Analyze problems to develop solutions involving computer hardware and software.
- Apply theoretical expertise and innovation to create or apply new technology, such as adapting principles for applying computers to new uses.
- Conduct logical analyses of business, scientific, engineering, and other technical problems, formulating mathematical models of problems for solution by computers.
- Evaluate project plans and proposals to assess feasibility issues.
- Consult with users, management, vendors, and technicians to determine computing needs and system requirements.
Which skills does a machine learning engineer need?
A machine learning engineer needs mathematical modelling, programming and the judgement to say when a project is not feasible. Show a problem you turned into a model, the measure you judged it by and what you would change.
O*NET's technology list for Computer and Information Research Scientists includes Apache Airflow, Apache Hadoop, Apache Hive and Amazon Redshift, all data infrastructure tools. Learn the modelling frameworks your target employers use as well; the O*NET list does not name them.
Which job titles should a machine learning engineer search for?
Machine learning work is posted under engineering and research titles. The titles O*NET reports for Computer and Information Research Scientists include Research Scientist, Computer Scientist and Scientific Programmer Analyst; search for Machine Learning Engineer by name too, since O*NET does not list it.
How do people use AI in machine learning engineering work?
0.08% of sampled Claude.ai conversations in May 2026 were about the tasks of Computer and Information Research Scientists, the closest O*NET occupation, according to the Anthropic Economic Index. In those conversations, 57.93% asked Claude to complete the task directly (automation) and 42.07% worked with it iteratively (augmentation).
Computer and Information Research Scientists is one of the occupations where automation outweighed augmentation in that sample. The figures describe how people used Claude, not the number of machine learning jobs.
Frequently asked questions
How is a machine learning engineer different from a data scientist?
A data scientist analyses data and validates models to inform decisions, while a machine learning engineer builds models that run inside a product. O*NET has an occupation for Data Scientists but none for machine learning engineers.
Which O*NET occupation is closest to a machine learning engineer?
Computer and Information Research Scientists (O*NET 15-1221.00) is the closest O*NET occupation. Data Scientists is the alternative when the job is mostly analysis.
Where this data comes from
- O*NET 31.0 DatabaseThis page includes information from the O*NET 31.0 Database by the U.S. Department of Labor, Employment and Training Administration (USDOL/ETA), used under the CC BY 4.0 license. O*NET® is a trademark of USDOL/ETA. EmployLabs has modified all or some of this information. USDOL/ETA has not approved, endorsed, or tested these modifications.
- Anthropic Economic Index, release_2026_06_26 (claude_ai, global, soc_occupation, May 2026)Anthropic Economic Index (Anthropic), data released under CC BY. Usage share is the share of sampled Claude.ai conversations classified to this occupation; the automation/augmentation split describes how those conversations used Claude. Neither measures the labour market.