Software Engineer Machine Learning
752.731 kr (SEK)/yr
361,89 kr (SEK)/hr
34.550 kr (SEK)/yr
The average software engineer machine learning gross salary in Gavle, Sweden is 752.731 kr or an equivalent hourly rate of 362 kr. This is 2% lower (-15.363 kr) than the average software engineer machine learning salary in Sweden. In addition, they earn an average bonus of 34.550 kr. Salary estimates based on salary survey data collected directly from employers and anonymous employees in Gavle, Sweden. An entry level software engineer machine learning (1-3 years of experience) earns an average salary of 526.545 kr. On the other end, a senior level software engineer machine learning (8+ years of experience) earns an average salary of 852.940 kr.
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841.167 kr (SEK)
12 %
Based on our compensation data, the estimated salary potential for Software Engineer Machine Learning will increase 12 % over 5 years.
This chart displays the highest level of education for:
Software Engineer Machine Learning, the majority at 100% with bachelors.
Typical Field of Study: Computer Programming, Specific Applications
See how education can impact your salaryThe cost of living in Gavle, Sweden is 3% less than the average cost of living in Sweden. Cost of living is calculated based on accumulating the cost of food, transportation, health services, rent, utilities, taxes, and miscellaneous.
View Cost of Living PageGävle ( YEV-lay or YEV-lə; Swedish: [ˈjɛ̌ːvlɛ] ) is a city in Sweden, the seat of Gävle Municipality and the capital and largest city of Gävleborg County. It had 79,004 inhabitants in 2020, which makes it the 13th-most-populated city in Sweden. It is the oldest city in Norrland, having received its charter in 1446 from Christopher of Bavaria and lying around 14 kilometres (8.7 mi) northwest of Dalälven, the traditional boundary line between Norrland and Svealand. The location of the city also gi
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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