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Tier 2 · mid · representative cell

Machine Learning Engineer — AI labor economics

Builds, trains, evaluates, and ships ML models to production. Owns feature engineering, model serving infrastructure, and ongoing model performance monitoring.

What follows is the matrix-derived read for this role at a representative cell. Your numbers will differ — compute your own Wagecard for the precise read on your geo, experience, salary, and task mix.

Operational AI cost
$9,138/mo
Tokens · oversight · retries · integration · orchestration.
Market rate (Tier 2 mid)
$180,400/yr
p25 $149,732 · p75 $212,872 · p90 $252,560
Economic substitution exposure
41 / 100
Lower = more human-leveraged. Augmentation territory.
Substitution distribution
By hours-weighted share across this role's tasks
Replaceable0%
AI-augmented33%
Human-led + AI-assisted50%
Human-critical17%

Per-task read

The capability and human-advantage scores driving the role-level numbers.

Task
Capability
Reliability
Error cost
Human-advantage
Feature engineering
65
55
3/5
50/100
Train and evaluate models
75
65
3/5
40/100
Deploy models to production
60
55
4/5
55/100
Monitor production models
65
60
4/5
50/100
Model architecture decisions
45
40
4/5
70/100
Cross-functional ML reviews
25
25
4/5
80/100

Compute your own Machine Learning Engineer Wagecard

Pick your geo, experience, salary, and the task mix that fits your actual week. Anonymous preview before sign-in; full result + share link with a free account.

Get my Wagecard →Live insights across geo × experienceSee methodology

Computed against capability matrix v1 · model v1-mvp · representative cell (Tier 2 / mid). Refreshed when the matrix refreshes. Open methodology at /methodology.