# Locomotive Engineers

> Drive electric, diesel-electric, steam, or gas-turbine-electric locomotives to transport passengers or freight. Interpret train orders, electronic or manual signals, and railroad rules and regulations.

- **SOC code:** 53-4011.00
- **Canonical URL:** https://singulariki.com/roles/role-53-4011-00
- **Also known as:** Locomotive Engineer, Passenger Locomotive Engineer, Railroad Engineer, Transportation Specialist, Through Freight Engineer, Train Engineer, Trainmaster, Diesel Engine Operator
- **Frame:** "AI exposure" means task overlap (how codifiable the work is), not jobs lost or a forecast. Every figure below is traced to a named public dataset.

## What this work is

**Core tasks** (O*NET):
- Interpret train orders, signals, or railroad rules and regulations that govern the operation of locomotives.
- Confer with conductors or traffic control center personnel via radiophones to issue or receive information concerning stops, delays, or oncoming trains.
- Receive starting signals from conductors and use controls such as throttles or air brakes to drive electric, diesel-electric, steam, or gas turbine-electric locomotives.
- Monitor gauges or meters that measure speed, amperage, battery charge, or air pressure in brake lines or in main reservoirs.
- Observe tracks to detect obstructions.
- Call out train signals to assistants to verify meanings.
- Operate locomotives to transport freight or passengers between stations or to assemble or disassemble trains within rail yards.
- Check to ensure that brake examination tests are conducted at shunting stations.
- Respond to emergency conditions or breakdowns, following applicable safety procedures and rules.
- Inspect locomotives to verify adequate fuel, sand, water, or other supplies before each run or to check for mechanical problems.
- Inspect locomotives after runs to detect damaged or defective equipment.
- Prepare reports regarding any problems encountered, such as accidents, signaling problems, unscheduled stops, or delays.

## Skills, tools, capabilities

**Knowledge, skills & abilities** (O*NET, highest importance first):
- Transportation _(knowledge)_
- Far Vision _(ability)_
- Operation and Control _(transferable_skill)_
- Operations Monitoring _(transferable_skill)_
- Selective Attention _(ability)_
- Control Precision _(ability)_
- Response Orientation _(ability)_
- Problem Sensitivity _(ability)_
- Reaction Time _(ability)_
- Near Vision _(ability)_
- Depth Perception _(ability)_
- Active Listening _(essential_skill)_

**Skills in demand:**
- Depth Perception _(Common Skill)_
- Active Listening _(Common Skill)_
- Information Ordering _(Specialized Skill)_
- Critical Thinking _(Common Skill)_
- Inductive Reasoning _(Common Skill)_
- Speech Recognition _(Specialized Skill)_
- Reading Comprehension _(Common Skill)_
- English Language _(Common Skill)_
- Deductive Reasoning _(Common Skill)_
- Time Management _(Common Skill)_
- Complex Problem Solving _(Common Skill)_
- Active Learning _(Common Skill)_

**Tools & technology:**
- Microsoft Excel _(hot technology)_
- Microsoft Word _(hot technology)_
- Electronic train management systems ETMS
- Route mapping software
- Time tracking software

## AI exposure & outlook

- **AI task-overlap index:** 33rd percentile (Low) across all occupations — composite of current-era exposure studies (ai-exposure-index-v1).
- **Overall AI exposure (Felten et al.):** 35th percentile (Moderate) — source: felten_aioe.
- **LLM task exposure, γ (OpenAI / Eloundou):** 36th percentile (Moderate) — source: eloundou_gamma.
- **AI assistant applicability (Microsoft):** 34th percentile (Moderate) — source: microsoft_applicability.
- **Frey–Osborne (2013, historical computerization estimate):** 91st percentile — kept separate from current-era studies.
- **Remote-capable (Dingel–Neiman):** no — task structure, not who actually works remote.
- **Projected employment (BLS 2024–34):** 0.7% growth (About average); 2.2k annual openings; 27k → 27.2k jobs.
- **Pay & employment (BLS OEWS, May 2024):** median $77,400; 31,990 employed.

## Sources

- **O*NET** (30.3) — U.S. Department of Labor / National Center for O*NET Development. https://www.onetcenter.org/database.html
- **BLS Occupational Employment and Wage Statistics (OEWS)** (May 2024) — U.S. Bureau of Labor Statistics. https://www.bls.gov/oes/
- **BLS Employment Projections** (2024–2034) — U.S. Bureau of Labor Statistics. https://www.bls.gov/emp/
- **Anthropic Economic Index** (v4 (2026-01-15) + v2 (2025-03-27)) — Anthropic. https://www.anthropic.com/economic-index
- **Microsoft “Working with AI”** (working-with-ai) — Microsoft Research. https://www.microsoft.com/en-us/research/
- **“GPTs are GPTs” (Eloundou et al.)** (arXiv 2303.10130) — OpenAI / academic. https://arxiv.org/abs/2303.10130
- **AI Occupational Exposure (AIOE)** (Felten, Raj & Seamans) — academic. https://github.com/AIOE-Data/AIOE
- **Frey & Osborne (2013)** (frey-osborne-automation) — academic. https://www.oxfordmartin.ox.ac.uk/publications/the-future-of-employment/
- **Dingel & Neiman (2020)** (dingel-neiman-workathome) — academic. https://github.com/jdingel/DingelNeiman-workathome

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_Generated from Singulariki's joined dataset; data snapshot 2026-06-02T21:00:32.945303+00:00. https://singulariki.com/roles/role-53-4011-00_