# Furnace, Kiln, Oven, Drier, and Kettle Operators and Tenders

> Operate or tend heating equipment other than basic metal, plastic, or food processing equipment. Includes activities such as annealing glass, drying lumber, curing rubber, removing moisture from materials, or boiling soap.

- **SOC code:** 51-9051.00
- **Canonical URL:** https://singulariki.com/roles/role-51-9051-00
- **Also known as:** Dry Kiln Operator, Furnace Operator, Kiln Fireman, Kiln Operator, Annealing Operator, Dryer Feeder, Evaporator Operator, Lime Kiln and Recausticizing 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):
- Monitor equipment operation, gauges, and panel lights to detect deviations from standards.
- Confer with supervisors or other equipment operators to report equipment malfunctions or to resolve production problems.
- Press and adjust controls to activate, set, and regulate equipment according to specifications.
- Record gauge readings, test results, and shift production in log books.
- Stop equipment and clear blockages or jams, using fingers, wire, or hand tools.
- Read and interpret work orders and instructions to determine work assignments, process specifications, and production schedules.
- Examine or test samples of processed substances, or collect samples for laboratory testing, to ensure conformance to specifications.
- Load equipment receptacles or conveyors with material to be processed, by hand or using hoists.
- Remove products from equipment, manually or using hoists, and prepare them for storage, shipment, or additional processing.
- Calculate amounts of materials to be loaded into furnaces, adjusting amounts as necessary for specific conditions.
- Transport materials and products to and from work areas, manually or using carts, handtrucks, or hoists.
- Melt or refine metal before casting, calculating required temperatures, and observe metal color, adjusting controls as necessary to maintain required temperatures.

## Skills, tools, capabilities

**Knowledge, skills & abilities** (O*NET, highest importance first):
- Operations Monitoring _(transferable_skill)_
- Problem Sensitivity _(ability)_
- Mechanical _(knowledge)_
- Control Precision _(ability)_
- Oral Comprehension _(ability)_
- Written Comprehension _(ability)_
- Arm-Hand Steadiness _(ability)_
- Near Vision _(ability)_
- Speech Recognition _(ability)_
- Production and Processing _(knowledge)_
- Public Safety and Security _(knowledge)_
- Reading Comprehension _(essential_skill)_

**Skills in demand:**
- Speech Recognition _(Specialized Skill)_
- Reading Comprehension _(Common Skill)_
- Information Ordering _(Specialized Skill)_
- Critical Thinking _(Common Skill)_
- Active Listening _(Common Skill)_
- Microsoft Word _(Common Skill)_
- Microsoft Excel _(Common Skill)_
- Inductive Reasoning _(Common Skill)_
- Deductive Reasoning _(Common Skill)_
- English Language _(Common Skill)_
- Finger Dexterity _(Common Skill)_
- Mathematics _(Common Skill)_

**Tools & technology:**
- Microsoft Excel _(hot technology)_
- Microsoft Word _(hot technology)_
- Inventory tracking software
- Machine operation software

## AI exposure & outlook

- **AI task-overlap index:** 24th percentile (Low) across all occupations — composite of current-era exposure studies (ai-exposure-index-v1).
- **Overall AI exposure (Felten et al.):** 19th percentile (Low) — source: felten_aioe.
- **LLM task exposure, γ (OpenAI / Eloundou):** 20th percentile (Low) — source: eloundou_gamma.
- **AI assistant applicability (Microsoft):** 39th percentile (Moderate) — source: microsoft_applicability.
- **Frey–Osborne (2013, historical computerization estimate):** 42nd percentile — kept separate from current-era studies.
- **Remote-capable (Dingel–Neiman):** no — task structure, not who actually works remote.
- **Projected employment (BLS 2024–34):** 3.0% growth (About average); 1.9k annual openings; 16.5k → 17k jobs.
- **Pay & employment (BLS OEWS, May 2024):** median $47,010; 16,160 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-51-9051-00_