Associate Data Scientist (Saudi Arabia)
Staffing and Recruiting Company Saudi Arabia
Al Khobar, Saudi Arabia
Machine Learning Engineers in Al Khobar, Saudi Arabia earn a median annual salary of $99,850 per year, which translates to approximately $8,320 per month. This median figure represents the middle point of all Machine Learning Engineers salaries in Al Khobar, Saudi Arabia, meaning 50% of professionals earn more than this amount, while 50% earn less.
Salaries for Machine Learning Engineers can vary widely depending on experience level. For example, an entry-level professional typically earns between $74,306 and $104,014 per year (6,192–8,667 per month). These ranges (from the 25th to 75th percentile) reflect typical earnings for each experience level, helping job seekers and employers understand what to expect in the current market.
The salary distribution for Machine Learning Engineers in Al Khobar also shows significant variation based on skills, company size, and industry. At the 25th percentile, Machine Learning Engineers earn $65,406 annually ($5,450 monthly), representing salaries at the lower end of the spectrum. The 75th percentile reaches $102,671 per year ($8,555 monthly), typically achieved by the most experienced Machine Learning Engineers with specialized skills.
Top-tier Machine Learning Engineers in Al Khobar, representing the 90th percentile, command salaries of $104,748 annually ($8,729 monthly) or higher.
The Saudi Arabia's tax-free income policy makes these salaries particularly attractive compared to other global tech hubs. A $95,000 salary in Al Khobar provides the same take-home pay as approximately $127,000 in London or $135,000 in New York after accounting for income taxes. This tax advantage, combined with Al Khobar's strategic location and growing tech ecosystem, continues to attract Machine Learning Engineer talent from around the world.
These salary ranges are based on 167 data points and salary submissions. Data includes base salaries from companies of all sizes across various industries. Learn more about our methodology →
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