# Tank Car, Truck, and Ship Loaders

> Load and unload chemicals and bulk solids, such as coal, sand, and grain, into or from tank cars, trucks, or ships, using material moving equipment. May perform a variety of other tasks relating to shipment of products. May gauge or sample shipping tanks and test them for leaks.

- **SOC code:** 53-7121.00
- **Canonical URL:** https://singulariki.com/roles/role-53-7121-00
- **Also known as:** Loader, Loader Operator, Tankerman, Truck Loader, Load Out Person, Loading Operator, Oil Movements Operator, PVC Loader (Polyvinyl Chloride Loader)
- **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):
- Seal outlet valves on tank cars, barges, and trucks.
- Verify tank car, barge, or truck load numbers to ensure car placement accuracy based on written or verbal instructions.
- Connect ground cables to carry off static electricity when unloading tanker cars.
- Check conditions and weights of vessels to ensure cleanliness and compliance with loading procedures.
- Start pumps and adjust valves or cables to regulate the flow of products to vessels, using knowledge of loading procedures.
- Observe positions of cars passing loading spouts, and swing spouts into the correct positions at the appropriate times.
- Monitor product movement to and from storage tanks, coordinating activities with other workers to ensure constant product flow.
- Copy and attach load specifications to loaded tanks.
- Remove and replace tank car dome caps, or direct other workers in their removal and replacement.
- Operate ship loading and unloading equipment, conveyors, hoists, and other specialized material handling equipment such as railroad tank car unloading equipment.
- Test samples for specific gravity, using hydrometers, or send samples to laboratories for testing.
- Test vessels for leaks, damage, and defects, and repair or replace defective parts as necessary.

## Skills, tools, capabilities

**Knowledge, skills & abilities** (O*NET, highest importance first):
- Control Precision _(ability)_
- Multilimb Coordination _(ability)_
- Operations Monitoring _(transferable_skill)_
- Operation and Control _(transferable_skill)_
- Manual Dexterity _(ability)_
- Rate Control _(ability)_
- Far Vision _(ability)_
- Problem Sensitivity _(ability)_
- Perceptual Speed _(ability)_
- Reaction Time _(ability)_
- Static Strength _(ability)_
- Depth Perception _(ability)_

**Skills in demand:**
- Depth Perception _(Common Skill)_
- English Language _(Common Skill)_
- Reading Comprehension _(Common Skill)_
- Time Management _(Common Skill)_
- Speech Recognition _(Specialized Skill)_
- Finger Dexterity _(Common Skill)_
- Writing _(Common Skill)_
- Visualization _(Specialized Skill)_
- Microsoft Excel _(Common Skill)_
- Linux _(Specialized Skill)_
- Information Ordering _(Specialized Skill)_
- Critical Thinking _(Common Skill)_

**Tools & technology:**
- Linux _(hot technology)_
- Microsoft Excel _(hot technology)_
- Microsoft Office software _(hot technology)_
- SAP software _(hot technology)_
- Warehouse management system WMS _(in demand)_
- CompuWeigh GMS
- Distributed control system DCS

## AI exposure & outlook

- **AI task-overlap index:** 9th percentile (Low) across all occupations — composite of current-era exposure studies (ai-exposure-index-v1).
- **Overall AI exposure (Felten et al.):** 7th percentile (Low) — source: felten_aioe.
- **LLM task exposure, γ (OpenAI / Eloundou):** 10th percentile (Low) — source: eloundou_gamma.
- **AI assistant applicability (Microsoft):** 23rd percentile (Low) — source: microsoft_applicability.
- **Frey–Osborne (2013, historical computerization estimate):** 59th percentile — kept separate from current-era studies.
- **Remote-capable (Dingel–Neiman):** no — task structure, not who actually works remote.
- **Projected employment (BLS 2024–34):** 4.3% growth (About average); 1.3k annual openings; 12k → 12.5k jobs.
- **Pay & employment (BLS OEWS, May 2024):** median $58,070; 10,920 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/
- **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-53-7121-00_