# Metal-Refining Furnace Operators and Tenders

> Operate or tend furnaces, such as gas, oil, coal, electric-arc or electric induction, open-hearth, or oxygen furnaces, to melt and refine metal before casting or to produce specified types of steel.

- **SOC code:** 51-4051.00
- **Canonical URL:** https://singulariki.com/roles/role-51-4051-00
- **Also known as:** Central Melt Specialist, Furnace Operator, Melter, Vacuum Melter, Arc and Argon Oxygen Decarburization Melter (ARC and AOD Melter), Automatic Furnace Operator, Control Room Operator, Electric Melt 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):
- Regulate supplies of fuel and air, or control flow of electric current and water coolant to heat furnaces and adjust temperatures.
- Draw smelted metal samples from furnaces or kettles for analysis, and calculate types and amounts of materials needed to ensure that materials meet specifications.
- Prepare material to load into furnaces, including cleaning, crushing, or applying chemicals, by using crushing machines, shovels, rakes, or sprayers.
- Weigh materials to be charged into furnaces, using scales.
- Record production data, and maintain production logs.
- Observe air and temperature gauges or metal color and fluidity, and turn fuel valves or adjust controls to maintain required temperatures.
- Operate controls to move or discharge metal workpieces from furnaces.
- Inspect furnaces and equipment to locate defects and wear.
- Drain, transfer, or remove molten metal from furnaces, and place it into molds, using hoists, pumps, or ladles.
- Remove impurities from the surface of molten metal, using strainers.
- Kindle fires, and shovel fuel and other materials into furnaces or onto conveyors by hand, with hoists, or by directing crane operators.
- Observe operations inside furnaces, using television screens, to ensure that problems do not occur.

## Skills, tools, capabilities

**Knowledge, skills & abilities** (O*NET, highest importance first):
- Production and Processing _(knowledge)_
- Operations Monitoring _(transferable_skill)_
- Control Precision _(ability)_
- Operation and Control _(transferable_skill)_
- Problem Sensitivity _(ability)_
- Manual Dexterity _(ability)_
- Near Vision _(ability)_
- Mechanical _(knowledge)_
- Monitoring _(essential_skill)_
- Arm-Hand Steadiness _(ability)_
- Reaction Time _(ability)_
- Selective Attention _(ability)_

**Skills in demand:**
- Information Ordering _(Specialized Skill)_
- Depth Perception _(Common Skill)_
- Deductive Reasoning _(Common Skill)_
- Reading Comprehension _(Common Skill)_
- Microsoft Excel _(Common Skill)_
- Inductive Reasoning _(Common Skill)_
- Finger Dexterity _(Common Skill)_
- Critical Thinking _(Common Skill)_
- Complex Problem Solving _(Common Skill)_
- Active Listening _(Common Skill)_
- Visualization _(Specialized Skill)_
- Time Management _(Common Skill)_

**Tools & technology:**
- Microsoft Excel _(hot technology)_
- Microsoft Office software _(hot technology)_
- Process control software
- Process safety management software
- Production tracking system software

## AI exposure & outlook

- **AI task-overlap index:** 13th percentile (Low) across all occupations — composite of current-era exposure studies (ai-exposure-index-v1).
- **Overall AI exposure (Felten et al.):** 12th percentile (Low) — source: felten_aioe.
- **LLM task exposure, γ (OpenAI / Eloundou):** 17th percentile (Low) — source: eloundou_gamma.
- **AI assistant applicability (Microsoft):** 20th percentile (Low) — source: microsoft_applicability.
- **Frey–Osborne (2013, historical computerization estimate):** 75th percentile — kept separate from current-era studies.
- **Remote-capable (Dingel–Neiman):** no — task structure, not who actually works remote.
- **Projected employment (BLS 2024–34):** -2.3% growth (Declining); 2k annual openings; 20.8k → 20.3k jobs.
- **Pay & employment (BLS OEWS, May 2024):** median $55,770; 20,330 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

---
_Generated from Singulariki's joined dataset; data snapshot 2026-06-02T21:00:32.945303+00:00. https://singulariki.com/roles/role-51-4051-00_