Machine Learning Engineer
657.054 kr. (DKK)/yr
315,89 kr. (DKK)/hr
30.159 kr. (DKK)/yr
The average machine learning engineer gross salary in Vejle, Denmark is 657.054 kr. or an equivalent hourly rate of 316 kr.. This is 8% lower (-55.364 kr.) than the average machine learning engineer salary in Denmark. In addition, they earn an average bonus of 30.159 kr.. Salary estimates based on salary survey data collected directly from employers and anonymous employees in Vejle, Denmark. An entry level machine learning engineer (1-3 years of experience) earns an average salary of 459.481 kr.. On the other end, a senior level machine learning engineer (8+ years of experience) earns an average salary of 744.104 kr..
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716.585 kr. (DKK)
9 %
Based on our compensation data, the estimated salary potential for Machine Learning Engineer will increase 9 % over 5 years.
This chart displays the highest level of education for:
Machine Learning Engineer, the majority at 100% with bachelors.
Typical Field of Study: Computer Programming, Specific Applications
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The cost of living in Vejle, Denmark is 8% less than the average cost of living in Denmark. Cost of living is calculated based on accumulating the cost of food, transportation, health services, rent, utilities, taxes, and miscellaneous.
View Cost of Living PageVejle (Danish pronunciation: [ˈvɑjlə]) is a city in Denmark, in the southeast of the Jutland Peninsula at the head of Vejle Fjord, where the Vejle River and Grejs River and their valleys converge. It is the site of the councils of Vejle Municipality (kommune) and the Region of Southern Denmark. The city has a population of 62,011 (As of 1 January. 2025), making it the ninth largest city in Denmark. Vejle Municipality has a population of 123,188 (as of January 2026), making it the fifth most pop
Sourced from WikipediaERI's compensation data are based on salary surveys conducted and researched by ERI. Cost of labor data in the Assessor Series are based on actual housing sales data from commercially available sources, plus rental rates, gasoline prices, consumables, medical care premium costs, property taxes, effective income tax rates, etc.
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