Alabuga AI and machine learning salaries

Across 40 open AI and machine learning postings at Alabuga that state a range, the middle half pays $18k–$46k a year, with a median of $38k. The lowest posted floor is $12k and the highest ceiling $52k. This is base pay as posted by Alabuga, converted to US dollars; equity and bonus are not included.

From pay ranges in open postingsRebuilt 12 Oct 2026

The range

Lowest floor$12k
25th percentile$18k
Median$38k
75th percentile$46k
Highest ceiling$52k

Postings by the midpoint of the posted range, in steps of $5k

Middle halfMedian bucket

Median by team at Alabuga

Engineering leadership$90k
Backend engineering$40k
Field and service$40k
AI and machine learning$38k
QA and testing$21k
Analytics$18k

Postings with a stated range: Engineering leadership 6 · Robotics 37 · Hardware engineering 42 · Backend engineering 16 · Field and service 18 · AI and machine learning 40 · QA and testing 7 · Analytics 6

AI and machine learning pay by level

LevelPostingsMedianMiddle half
Senior6$47k$47k–$47k
Lead3$46k$46k–$46k

40 AI and machine learning roles open at Alabuga now

40 of them show the pay range on the posting.

Questions

How much does Alabuga pay for AI and machine learning roles?

The median of the 40 posted ranges is $38k a year; the middle half sits between $18k and $46k.

Is this base salary or total compensation?

Base salary as written in the posting. Many US employers post ranges under state pay transparency laws (California, New York, Washington, Colorado and others); equity and bonus are not part of these figures.

Why posted ranges and not self-reported salaries?

A posted range is what the employer commits to for a role it has open today. It is fresher than a survey and cannot be inflated by respondents. Each posting counts once, at the midpoint of its range.

Which level pays most?

Senior roles have the highest median here: $47k a year across 6 postings.

Source: the pay ranges of 40 open postings on Alabuga's careers board; the midpoint of each rangeUpdated 12 Oct 2026MethodologyJSONCC BY-SA 4.0