Headline risk
27%
Moderate RiskRail-track laying and maintenance equipment operators
United States AI Work Index tracks this occupation on the shared structural baseline and then layers on local demand resilience, wages, and confidence.
Why This Score
Share of job tasks that overlap with current AI capabilities
Median annual wage
Projected employment change over 10 years
Typical preparation needed for this occupation
Occupation profile
Lay, repair, and maintain track for standard or narrow-gauge railroad equipment used in regular railroad service or in plant yards, quarries, sand and gravel pits, and mines. Includes ballast cleaning machine operators and railroad bed tamping machine operators.
Task evidence
100% weighted task match · 0% effective coverage
Scores combine AI task overlap, human advantages, and local demand. How it works
United States Now
Median Wage
USD 67,370
Employment 2024
15.0K
Projected Change (2024–34)
1.6%
Openings (2024–34)
1.1K
Wage distribution
Demand outlook
Projections published, but no prose outlook available.
Role Profile
Tasks
- 1. Repair or adjust track switches, using wrenches and replacement parts. AI use: 0%
- 2. Patrol assigned track sections so that damaged or broken track can be located and reported. AI use: 0%
- 3. Cut rails to specified lengths, using rail saws. AI use: 0%
- 4. Observe leveling indicator arms to verify levelness and alignment of tracks. AI use: 0%
- 5. Operate track wrenches to tighten or loosen bolts at joints that hold ends of rails together. AI use: 0%
- 6. Clean tracks or clear ice or snow from tracks or switch boxes. AI use: 0%
Technologies
Requirements
Work context
Worker profile
Median age 40.9 · 404K employed
Under 25: 14% · 25–54: 65% · 55+: 21%
Related
Source coverage
11/11 source families · O*NET 30.2 / OEWS 2024 / ORS 2025 / OOH 2025-08-28 / Projections 2024-34 / CPS 2025 / Anthropic task penetration
Mapping quality
title_match · employment series present
Narrative & sources
Published limitations
This page shows the local country layer, not realised individual job outcomes. The global structural baseline is shared across countries; only the local demand and wage layer changes here.
Built from O*NET occupation descriptions, task statements, technology skills, work context, Job Zones, Anthropic task penetration, BLS OEWS wages, BLS projection tables, BLS ORS requirements, BLS OOH narrative content, BLS skills data, and BLS CPS occupation age tables.