# Ship Engineers

> Supervise and coordinate activities of crew engaged in operating and maintaining engines, boilers, deck machinery, and electrical, sanitary, and refrigeration equipment aboard ship.

- **SOC code:** 53-5031.00
- **Canonical URL:** https://singulariki.com/roles/role-53-5031-00
- **Also known as:** Engineer, Ferry Engineer, Port Engineer, Tug Boat Engineer, Barge Engineer, Harbor Engineer, Ship Engineer, Towboat Engineer
- **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):
- Monitor engine, machinery, or equipment indicators when vessels are underway, and report abnormalities to appropriate shipboard staff.
- Monitor the availability, use, or condition of lifesaving equipment or pollution preventatives to ensure that international regulations are followed.
- Monitor and test operations of engines or other equipment so that malfunctions and their causes can be identified.
- Start engines to propel ships, and regulate engines and power transmissions to control speeds of ships, according to directions from captains or bridge computers.
- Perform or participate in emergency drills, as required.
- Perform general marine vessel maintenance or repair work, such as repairing leaks, finishing interiors, refueling, or maintaining decks.
- Maintain or repair engines, electric motors, pumps, winches, or other mechanical or electrical equipment, or assist other crew members with maintenance or repair duties.
- Maintain complete records of engineering department activities, including machine operations.
- Operate or maintain off-loading liquid pumps or valves.
- Maintain electrical power, heating, ventilation, refrigeration, water, or sewerage systems.
- Install engine controls, propeller shafts, or propellers.
- Clean engine parts and keep engine rooms clean.

**Emerging tasks** (O*NET):
- Use drone technology for ship inspections, maintenance, or other tasks.

## Skills, tools, capabilities

**Knowledge, skills & abilities** (O*NET, highest importance first):
- Mechanical _(knowledge)_
- Oral Comprehension _(ability)_
- Oral Expression _(ability)_
- Problem Sensitivity _(ability)_
- Deductive Reasoning _(ability)_
- Critical Thinking _(essential_skill)_
- Operations Monitoring _(transferable_skill)_
- Operation and Control _(transferable_skill)_
- Equipment Maintenance _(transferable_skill)_
- Troubleshooting _(transferable_skill)_
- Repairing _(transferable_skill)_
- Active Listening _(essential_skill)_

**Skills in demand:**
- Deductive Reasoning _(Common Skill)_
- Critical Thinking _(Common Skill)_
- Equipment Maintenance _(Specialized Skill)_
- Active Listening _(Common Skill)_
- English Language _(Common Skill)_
- Speech Recognition _(Specialized Skill)_
- Complex Problem Solving _(Common Skill)_
- Systems Analysis _(Specialized Skill)_
- Mathematics _(Common Skill)_
- Information Ordering _(Specialized Skill)_
- Inductive Reasoning _(Common Skill)_
- Active Learning _(Common Skill)_

**Tools & technology:**
- Apple macOS _(hot technology)_
- Microsoft Access _(hot technology)_
- Microsoft Excel _(hot technology)_
- Microsoft Office software _(hot technology)_
- Microsoft Outlook _(hot technology)_
- Microsoft PowerPoint _(hot technology)_
- Microsoft Project _(hot technology)_
- Microsoft Word _(hot technology)_
- Oracle Database _(hot technology)_
- Salesforce software _(hot technology)_
- SAP software _(hot technology)_
- Computer aided dispatch software

## AI exposure & outlook

- **AI task-overlap index:** 20th percentile (Low) across all occupations — composite of current-era exposure studies (ai-exposure-index-v1).
- **Overall AI exposure (Felten et al.):** 34th percentile (Moderate) — source: felten_aioe.
- **LLM task exposure, γ (OpenAI / Eloundou):** 27th percentile (Low) — source: eloundou_gamma.
- **AI assistant applicability (Microsoft):** 4th percentile (Low) — source: microsoft_applicability.
- **Frey–Osborne (2013, historical computerization estimate):** 22nd percentile — kept separate from current-era studies.
- **Remote-capable (Dingel–Neiman):** no — task structure, not who actually works remote.
- **Projected employment (BLS 2024–34):** 1.6% growth (About average); 1.1k annual openings; 8.8k → 9k jobs.
- **Pay & employment (BLS OEWS, May 2024):** median $101,320; 8,580 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-53-5031-00_