Machine Learning Ops Engineer Salary in Illinois
$30.5K - $44.6K
1% of jobs
$44.6K - $58.6K
1% of jobs
$58.6K - $72.7K
6% of jobs
$72.7K - $86.7K
6% of jobs
$97K is the 25th percentile. Wages below this are outliers.
$86.7K - $100.8K
14% of jobs
$100.8K - $114.8K
13% of jobs
The median wage is $121.3K / yr.
$114.8K - $128.9K
17% of jobs
$128.9K - $142.9K
13% of jobs
$145.6K is the 75th percentile. Wages above this are outliers.
$142.9K - $157K
12% of jobs
$157K - $171K
10% of jobs
$171K - $185.1K
5% of jobs
$30.5K
$126K
$187K
$124,780
/year
$59.99
/hour
Machine Learning Ops Engineer Salary Comparison by Location
Nationwide
United States
$128,769
Illinois
Illinois
$124,780
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How much does a Machine Learning Ops Engineer make in Illinois?
As of Aug 21, 2026, the average annual pay for a Machine Learning Ops Engineer in Illinois is $124,780 a year.
Just in case you need a simple salary calculator, that works out to be approximately $59.99 an hour. This is the equivalent of $2,399/week or $10,398/month.
While ZipRecruiter is seeing salaries as high as $187,022 and as low as $30,524, the majority of Machine Learning Ops Engineer salaries currently range between $98,400 (25th percentile) to $150,200 (75th percentile) with top earners (90th percentile) making $172,486 annually in Illinois. The average pay range for a Machine Learning Ops Engineer varies greatly (by as much as 51800), which suggests there may be many opportunities for advancement and increased pay based on skill level, location and years of experience.
Illinois ranks number 50 out of 50 states nationwide for Machine Learning Ops Engineer salaries.
To estimate the most accurate annual salary range for Machine Learning Ops Engineer jobs, ZipRecruiter continuously scans its database of millions of active jobs published locally throughout America.
Find your next high paying job as a Machine Learning Ops Engineer on ZipRecruiter today.
What are Top 10 Highest Paying Cities for Machine Learning Ops Engineer Jobs in Illinois
We've identified 10 cities where the typical salary for a Machine Learning Ops Engineer job is above the average in Illinois in Illinois. Topping the list is Winnetka, , with Mettawa, and Carol Stream, close behind in the second and third positions. Carol Stream, beats the average in Illinois by $8,406 (6.7%), and Winnetka, furthers that trend with another $26,573 (21.3%) above the $124,780 average.
With these 10 cities paying on average above the average for Illinois, the opportunities for economic advancement by changing locations as a Machine Learning Ops Engineer appears to be exceedingly fruitful.
Finally, another factor to consider is the average salary for these top 10 cities varies very little at 16% between Winnetka, and Naperville, , reinforcing the limited potential for much wage advancement. The possibility of a lower cost of living may be the best factor to use when considering location and salary for a Machine Learning Ops Engineer role.
City | Annual Salary | Hourly Wage |
|---|---|---|
Winnetka | $151,353 | $72.77 |
Mettawa | $147,434 | $70.88 |
Carol Stream | $133,186 | $64.03 |
Chicago | $132,651 | $63.77 |
Buffalo Grove | $132,059 | $63.49 |
60089 | $131,944 | $63.43 |
Mundelein | $131,462 | $63.20 |
Deerfield | $130,950 | $62.96 |
Niles | $129,458 | $62.24 |
Naperville | $128,654 | $61.85 |
What are Top 5 Best Paying Related Machine Learning Ops Engineer Jobs in Illinois
Analyzing similar jobs related to the Machine Learning Ops Engineer job category in Illinois, we found 5 that were relevant. However, none pay more than the $124780 state average for Machine Learning Ops Engineer jobs.
Nevertheless Architecture Faculty, Nuclear Physics Professor, or Suny Research Foundation may still be interesting positions to explore.
Job Title | Annual Salary | Hourly Wage |
|---|---|---|
Architecture Faculty | $95,736 | $46.03 |
Nuclear Physics Professor | $83,792 | $40.28 |
Suny Research Foundation | $55,578 | $26.72 |
Customer Development Specialist | $54,453 | $26.18 |
Nano | $49,729 | $23.91 |
About Our Data
ZipRecruiter salary estimates, histograms, trends and comparisons are derived from both employer job postings and third party data sources.
