ML Engineer vs Data Scientist
ML Engineers and Data Scientists often work side by side, but their responsibilities differ in a critical way. Data Scientists design and evaluate models — the research and experimentation layer. ML Engineers take those models and build the systems to deploy, serve and monitor them in production. The distinction matters more as teams grow, and the hiring market reflects it clearly in skills required and pay.
ML Engineer
1,097
open roles
Data Scientist
1,046
open roles
What the data shows
ML Engineer roles account for 51% of active listings between these two roles — 51 more open positions than Data Scientist right now.
ML Engineer commands a higher median salary — $134,200 vs $119,353 — a gap of roughly 12%. Both roles show meaningful upside at the 75th percentile.
ML Engineer skews more remote-friendly — 16% of listings offer remote work vs 15% for Data Scientist, a 1-point difference.
ML Engineer skews more senior — 50% of its listings target senior-level candidates, reflecting the depth of specialization the role demands.
Salary comparison
ML Engineer
P25
$97,844
Median
$134,200
P75
$182,392
Data Scientist
P25
$82,022
Median
$119,353
P75
$161,863
Salary estimates derived from active listings that include explicit pay ranges. Midpoint of stated range used to compute percentiles.
Shared skills
These skills appear in the top 12 for both roles — useful if you're considering a transition or working across both areas.
Data from active job listings · Updated hourly