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Singulariki

Rail Car Repairers

Occupation · SOC 49-3043.00

Diagnose, adjust, repair, or overhaul railroad rolling stock, mine cars, or mass transit rail cars.

Also called: Rail Car Mechanic · Rail Car Repairer · Rail Car Repairman · Rail Car Welder · Freight Maintenance Specialist · Locomotive Repairman · Rail Car Maintenance Mechanic · Rail Car Sandblaster · Railroad Car Repairman · Train Car Repairman · Air Brake Adjuster · Air Brake Man

Job family: Installation, Maintenance, and Repair Occupations

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A source-stamped Markdown brief of this occupation — paste it into an agent, or fetch /roles/role-49-3043-00/context.md directly.

AI work map

A fast read on where AI already shows up in this occupation, where it stays a copilot, where humans remain in the loop, and what the labor market is doing. Built from observed Claude.ai conversations mapped to O*NET tasks and from published research — measures of usage and exposure, not advice or predictions that the job is going away.

6th-percentile task overlap — yet about 1,500 openings a year (+2.8% projected, BLS) . What exposure means →

AI & job outlook

What today's research says about this occupation's exposure to AI, how AI is actually being used in it, and where employment is headed. These are positions within published studies — measures of exposure and usage, not predictions that this job will disappear.

Exposure to current AI

Each study uses its own scale, so the raw scores are not comparable across rows — the percentile (this job's rank among all U.S. occupations with data) is the comparable figure, and sizes the bars.

Measure Rank vs all occupations Percentile Score
Overall AI exposure (Felten et al.) Low 6th -1.4
LLM task exposure, γ (OpenAI / Eloundou) Low 17th 0.1
AI assistant applicability (Microsoft) Low 8th 0.0

OpenAI's exposure study scores tasks three ways: with a language model alone (α 0.1), with simple added tooling (β 0.1), and including AI-powered software (γ 0.1). Higher means more of the job's tasks could be done at least twice as fast — not that they will be automated away.

This job mostly cannot be done remotely (Dingel–Neiman) — its hands-on tasks sit outside what software-based AI reaches.

Mixed signals. Today's AI/LLM studies show relatively low exposure for this job, but the older (2013) Frey–Osborne work rated it higher for computerization and robotics. Different eras, different technologies — the AI measures above reflect the current state.

Historical automation estimate (2013)

A pre-LLM (2013) estimate of how automatable this job is by computerization and robotics. Shown for historical context only — it is not part of any current AI ranking.

Frey–Osborne probability 0.9 · 75th percentile among occupations · High

Job outlook

Independent U.S. Bureau of Labor Statistics employment projection for 2024–2034 — a labor-market forecast, not an AI-impact forecast.

Outlook About average · +2.8% by 2034
Projected annual openings 1,500
Employment 2024 → 2034 17,900 → 18,400

“Annual openings” counts new jobs plus replacements for workers who leave the occupation, so it can be large even when growth is modest.

Where this work sits on the global GenAI gradient

The ILO's 2025 global study scores generative-AI exposure on the international ISCO-08 occupation system, not US SOC. Bridged through the published (and approximate, many-to-many) IBS O*NET-SOC ↔ ISCO-08 crosswalk, this US occupation corresponds to the international occupation below. Exposure here means how much of the work's tasks today's AI can attempt — task overlap, not automation, adoption, or jobs lost.

17% mean task exposure (2025)
24th percentile of 427 placed occupations
−2 pts shift 2023 → 2025
International occupation (ISCO-08) Task exposure (2025) Most tasks fall in
Agricultural and Industrial Machinery Mechanics and Repairers · 7233 17% Not exposed

Read the whole six-band gradient on the GenAI exposure gradient page. The crosswalk is approximate: a US occupation can map to several international ones, and the ILO scores describe the international occupation, not this exact US role.

Tasks

All 20 tasks O*NET lists for this occupation, ordered by importance. Each links to its own page with AI-exposure and observed-use detail.

Work activities

Knowledge, skills & abilities

O*NET importance rating, from 1 (not important) to 5 (extremely important).

Knowledge

Mechanical 4.5
Production and Processing 3.4
Design 3.3
Administration and Management 3.2
Building and Construction 3.2
Engineering and Technology 3.2
Transportation 3.1
Mathematics 3.0

Transferable skills

Troubleshooting 4.0
Repairing 4.0
Equipment Maintenance 3.8
Operations Monitoring 3.3
Quality Control Analysis 3.3
Operation and Control 3.1

Abilities

Arm-Hand Steadiness 3.9
Manual Dexterity 3.9
Control Precision 3.9
Multilimb Coordination 3.8
Finger Dexterity 3.6
Near Vision 3.6
Problem Sensitivity 3.5
Reaction Time 3.5
Static Strength 3.5
Trunk Strength 3.5
Perceptual Speed 3.4
Flexibility of Closure 3.3
Extent Flexibility 3.3
Deductive Reasoning 3.1
Information Ordering 3.1
Visualization 3.1
Far Vision 3.1
Inductive Reasoning 3.0
Category Flexibility 3.0
Selective Attention 3.0
Stamina 3.0
Gross Body Coordination 3.0
Gross Body Equilibrium 3.0

Essential skills

Critical Thinking 3.3
Active Listening 3.0
Speaking 3.0

Skills in demand

Skills employers ask for in job postings for this occupation (Lightcast), with whether each is a common or specialized skill.

Tools & technology

Example Category
Adobe Acrobat Document management software Hot technology
Microsoft Excel Spreadsheet software Hot technology
Microsoft Office software Office suite software Hot technology
Microsoft Outlook Electronic mail software Hot technology
Microsoft PowerPoint Presentation software Hot technology
Microsoft Windows Operating system software Hot technology
Microsoft Word Word processing software Hot technology
Mozilla Firefox Internet browser software Hot technology
Disassembler software Compiler and decompiler software In demand
Microsoft Internet Explorer Internet browser software
RailTech Software Solutions Rail 21 Management System Inventory management software
RailTech Software Systems Mars for the 21st Century Accounting software
WheelShop Automation.com Wheel Shop Management Suite Data base user interface and query software

Work context

How characteristic each condition is of the job, on O*NET's 1–5 context scale (higher = more present in day-to-day work). Each condition links to how it varies across all occupations.

Wear Common Protective or Safety Equipment such as Safety Shoes, Glasses, Gloves, Hearing Protection, Hard Hats, or Life Jackets 4.9
Spend Time Using Your Hands to Handle, Control, or Feel Objects, Tools, or Controls 4.9
Spend Time Standing 4.6
Exposed to Sounds, Noise Levels that are Distracting or Uncomfortable 4.6
Face-to-Face Discussions with Individuals and Within Teams 4.5
Exposed to Contaminants 4.5
Exposed to Extremely Bright or Inadequate Lighting Conditions 4.5
Indoors, Not Environmentally Controlled 4.4
Exposed to Very Hot or Cold Temperatures 4.3
Outdoors, Exposed to All Weather Conditions 4.3
Freedom to Make Decisions 4.1
Exposed to Hazardous Equipment 4.0
Work With or Contribute to a Work Group or Team 4.0
Time Pressure 4.0
Contact With Others 4.0
Exposed to Minor Burns, Cuts, Bites, or Stings 3.9
In an Open Vehicle or Operating Equipment 3.9
Importance of Being Exact or Accurate 3.8
Coordinate or Lead Others in Accomplishing Work Activities 3.7
Determine Tasks, Priorities and Goals 3.7
Consequence of Error 3.6
Physical Proximity 3.6
Spend Time Walking or Running 3.6
Spend Time Bending or Twisting Your Body 3.6
Health and Safety of Other Workers 3.6
Wear Specialized Protective or Safety Equipment such as Breathing Apparatus, Safety Harness, Full Protection Suits, or Radiation Protection 3.6
Exposed to High Places 3.5
In an Enclosed Vehicle or Operate Enclosed Equipment 3.5
Exposed to Cramped Work Space, Awkward Positions 3.4
Impact of Decisions on Co-workers or Company Results 3.4
Exposed to Hazardous Conditions 3.4
Spend Time Kneeling, Crouching, Stooping, or Crawling 3.4
Spend Time Making Repetitive Motions 3.4
Frequency of Decision Making 3.0
Work Outcomes and Results of Other Workers 2.9
Deal With External Customers or the Public in General 2.8
Outdoors, Under Cover 2.8
Importance of Repeating Same Tasks 2.8
Spend Time Keeping or Regaining Balance 2.7
Telephone Conversations 2.6

How to get in

Job zone
Zone 2 — Job Zone 1-2: Very Little to Some Preparation Needed
Education
Usually requires a high school diploma or GED, though some occupations may not.
Typical entry-level education
High school diploma or equivalent · BLS, the typical path — not a requirement
Related experience
Some occupations may need little or no previous experience; others require several months to a year of experience. For example, landscaping and groundskeeping workers might require very little training or previous experience, while agricultural equipment operators can benefit from on-the job training.
Preparation level
SVP (Below 6.0) — total schooling plus on-the-job experience.

What to study: Mechanic and Repair Technologies/Technicians . Fields of study crosswalked to this occupation (NCES CIP–SOC), not a requirement.

Education of current workers

Share of people in this occupation at each level of education.

High School Diploma 61.4%
Post-Secondary Certificate 37.6%
Associate's Degree (or other 2-year degree) 1.0%

Interests & work styles

The interests and personal qualities O*NET associates with people who do this work.

Career interests (Holland / RIASEC)

Realistic 7.0
Conventional 3.9
Investigative 2.8
Social 1.5

Interest areas

Mechanics/Electronics 6.5
Physical/Manual Labor 6.2
Engineering 4.0
Transportation/Machine Operation 2.6
Construction/Woodwork 2.2
Mathematics/Statistics 1.5

Work styles

Dependability 3.0
Attention to Detail 2.4
Cautiousness 2.0
Perseverance 1.5
Integrity 1.4
Stress Tolerance 1.3

Wages & employment

U.S. · annual wages (BLS OEWS)

$46k10th$52k25th$66kMedian$80k75th$92k90th
Annual wages by percentile — U.S. (BLS OEWS). The light band spans the 10th–90th percentile; the darker band is the middle half (25th–75th); the line is the median.
18k202418k2034 (proj.)+2.8% · About average
Projected U.S. employment, 2024–2034 (BLS Employment Projections). A labor-market forecast for the occupation, not an AI-impact forecast.
10th percentile $45,670
25th percentile $51,640
Median (50th) $65,680
75th percentile $80,150
90th percentile $92,000
People employed 18,300

Industries that employ this occupation

Where these workers are employed, by number of jobs (national, BLS OEWS). Pay shown is the occupation's national median, not industry-specific.

Industry Workers National median pay
Transportation and Warehousing · Sector 15,650 $63,100
Manufacturing · Sector 430 $57,330
Mining, Quarrying, and Oil and Gas Extraction · Sector 30 $79,830
Administrative and Support and Waste Management and Remediation Services · Sector $61,020

Where this work is most concentrated

Industries where this occupation is far more common than in the economy as a whole. The location quotient is how many times more concentrated it is here (a value of 5 means five times its economy-wide share).

Industry Concentration Workers
Transportation and Warehousing · Sector 17.84× 15,650
Manufacturing · Sector 0.28× 430

Part of the Supply Chain & Transportation career cluster.

Exposure quadrant: AI task-overlap percentile vs Median pay Rail Car Repairers sits at the 6th percentile of AI task-overlap and the 56th percentile of median pay, placed here against 12 adjacent occupations on the same two axes. Lower overlap · higher pay Higher overlap · higher pay Higher overlap · lower pay Lower overlap · lower pay Rail Car Repairers Aircraft Structure, Surfaces, Rigging, and Systems Assemblers Motorcycle Mechanics Automotive Body and Related Repairers Aircraft Mechanics and Service Technicians Control and Valve Installers and Repairers, Except Mechanical Door AI task-overlap percentile → ↑ Median pay
AI task-overlap percentile (horizontal) vs. median-pay percentile (vertical), across all scored occupations. This occupation is highlighted; related occupations are plotted alongside it. Overlap measures shared tasks with AI, not automation.

Side-by-side comparisons place two occupations’ pay, preparation, skills, and AI exposure on the same page — same data, same scale, no forecast.

What you can do with this

Options the data surfaces for Rail Car Repairers — not advice or a forecast. Each is a real cross-link you can follow into the evidence.

Skills that travel

Capabilities this work builds that are used across many other occupations.

Paths in

How people typically prepare for this work.

Zoom out

On the global GenAI exposure gradient this work sits around the 24th percentile of 427 international occupations.

Write a report on thisheadline · factoids · citation

Rail Car Repairers show 6th-percentile AI task overlap — and about 1,500 annual U.S. openings

  • Rail Car Repairers rank in the 6th percentile (Low band) for AI task overlap across U.S. occupations — a measure of how much of the work today's AI can attempt, not how much is automated.Eloundou et al. (GPTs are GPTs) + Felten AIOE
  • The occupation is projected to see about 1,500 U.S. job openings per year (2024–34), counting growth and replacement — a labor-demand projection made independently of AI.BLS Employment Projections 2024–34
  • BLS projects employment to be about average (+2.8%) from 2024 to 2034.BLS Employment Projections 2024–34
  • Median annual pay is $65,680, across about 18,300 U.S. workers.BLS OEWS (May 2024)
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Rail Car Repairers show 6th-percentile AI task overlap — and about 1,500 annual U.S. openings

• Rail Car Repairers rank in the 6th percentile (Low band) for AI task overlap across U.S. occupations — a measure of how much of the work today's AI can attempt, not how much is automated. (Eloundou et al. (GPTs are GPTs) + Felten AIOE)
• The occupation is projected to see about 1,500 U.S. job openings per year (2024–34), counting growth and replacement — a labor-demand projection made independently of AI. (BLS Employment Projections 2024–34)
• BLS projects employment to be about average (+2.8%) from 2024 to 2034. (BLS Employment Projections 2024–34)
• Median annual pay is $65,680, across about 18,300 U.S. workers. (BLS OEWS (May 2024))

Source: Singulariki — "Rail Car Repairers". https://singulariki.com/roles/role-49-3043-00
Note: AI task overlap measures what today's AI can attempt, not automation, job loss, or a forecast.

AssetsShare imageMethodology & sourcesPress & newsroomThe newsroom

Every line is built only from figures this page already shows and cites. AI task overlap means what today's AI can attempt — not automation, job loss, or a forecast.

Sources for this page

Every figure above traces to a named public dataset and the exact release below — not hand-written opinion. See the full methodology for what each measure does and does not mean.

Data compiled June 2, 2026. Figures are estimates, not advice.

Cite this page
Plain

Singulariki. "Rail Car Repairers." Singulariki: a source-backed encyclopedia of work. Built from O*NET 30.3; BLS Occupational Employment and Wage Statistics (OEWS) May 2024; BLS Employment Projections 2024–2034; Microsoft “Working with AI” working-with-ai; “GPTs are GPTs” (Eloundou et al.) arXiv 2303.10130; AI Occupational Exposure (AIOE) Felten, Raj & Seamans; ILO / Gmyrek et al. GenAI exposure gradient 2025; IBS O*NET-SOC ↔ ISCO-08 occupation crosswalk 2022; Frey & Osborne (2013) frey-osborne-automation; Dingel & Neiman (2020) dingel-neiman-workathome. Accessed June 7, 2026. https://singulariki.com/roles/role-49-3043-00

APA

Singulariki. (2026). Rail Car Repairers. Singulariki: a source-backed encyclopedia of work. Retrieved June 7, 2026, from https://singulariki.com/roles/role-49-3043-00

BibTeX
@misc{singulariki-role-49-3043-00,
  title  = {Rail Car Repairers},
  author = {{Singulariki}},
  year   = {2026},
  note   = {O*NET 30.3; BLS Occupational Employment and Wage Statistics (OEWS) May 2024; BLS Employment Projections 2024–2034; Microsoft “Working with AI” working-with-ai; “GPTs are GPTs” (Eloundou et al.) arXiv 2303.10130; AI Occupational Exposure (AIOE) Felten, Raj & Seamans; ILO / Gmyrek et al. GenAI exposure gradient 2025; IBS O*NET-SOC ↔ ISCO-08 occupation crosswalk 2022; Frey & Osborne (2013) frey-osborne-automation; Dingel & Neiman (2020) dingel-neiman-workathome. Accessed June 7, 2026},
  url    = {https://singulariki.com/roles/role-49-3043-00}
}

Citations name the underlying public dataset releases — they reflect what this page is built from, not just the URL.

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