Skills it runs on
The capabilities O*NET rates most important for this occupation — the human ground the work is built on.
See all skills →Furnace, Kiln, Oven, Drier, and Kettle Operators and Tenders
Occupation · SOC 51-9051.00
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.
Also called: Dry Kiln Operator · Furnace Operator · Kiln Fireman · Kiln Operator · Annealing Operator · Dryer Feeder · Evaporator Operator · Lime Kiln and Recausticizing Operator · Oven Operator · Ager Operator · Annealer · Autoclave Operator
Job family: Production Occupations
Take this to your AI
A source-stamped Markdown brief of this occupation — paste it into an agent, or fetch
/roles/role-51-9051-00/context.md directly.
AI work map
A fast read on where AI already shows up in this occupation, where it stays a copilot, where humans remain in the loop, and what the labor market is doing. Built from observed Claude.ai conversations mapped to O*NET tasks and from published research — measures of usage and exposure, not advice or predictions that the job is going away.
Market signal
Independent published positions, read together — not a forecast.
- About average employment outlook (+3.0% by 2034)
- 1,900 openings/yr
- Moderate AI exposure
- Median pay $47,010/yr
- Work is mostly on-site (not teleworkable)
24th-percentile task overlap — yet about 1,900 openings a year (+3% projected, BLS) . What exposure means →
AI & job outlook
What today's research says about this occupation's exposure to AI, how AI is actually being used in it, and where employment is headed. These are positions within published studies — measures of exposure and usage, not predictions that this job will disappear.
Exposure to current AI
Each study uses its own scale, so the raw scores are not comparable across rows — the percentile (this job's rank among all U.S. occupations with data) is the comparable figure, and sizes the bars.
| Measure | Rank vs all occupations | Percentile | Score |
|---|---|---|---|
| Overall AI exposure (Felten et al.) Low | 19th | -1.0 | |
| LLM task exposure, γ (OpenAI / Eloundou) Low | 20th | 0.2 | |
| AI assistant applicability (Microsoft) Moderate | 39th | 0.1 |
OpenAI's exposure study scores tasks three ways: with a language model alone (α 0.2), with simple added tooling (β 0.2), and including AI-powered software (γ 0.2). Higher means more of the job's tasks could be done at least twice as fast — not that they will be automated away.
This job mostly cannot be done remotely (Dingel–Neiman) — its hands-on tasks sit outside what software-based AI reaches.
Historical automation estimate (2013)
A pre-LLM (2013) estimate of how automatable this job is by computerization and robotics. Shown for historical context only — it is not part of any current AI ranking.
Frey–Osborne probability 0.4 · 42nd percentile among occupations · Moderate
Job outlook
Independent U.S. Bureau of Labor Statistics employment projection for 2024–2034 — a labor-market forecast, not an AI-impact forecast.
| Outlook | About average · +3.0% by 2034 |
| Projected annual openings | 1,900 |
| Employment 2024 → 2034 | 16,500 → 17,000 |
“Annual openings” counts new jobs plus replacements for workers who leave the occupation, so it can be large even when growth is modest.
Where this work sits on the global GenAI gradient
The ILO's 2025 global study scores generative-AI exposure on the international ISCO-08 occupation system, not US SOC. Bridged through the published (and approximate, many-to-many) IBS O*NET-SOC ↔ ISCO-08 crosswalk, this US occupation corresponds to the international 3 occupations below. Exposure here means how much of the work's tasks today's AI can attempt — task overlap, not automation, adoption, or jobs lost.
Furnace, Kiln, Oven, Drier, and Kettle Operators and Tenders sits at the 31st percentile of 427
occupations on the global GenAI task-exposure gradient
— exposure rose from 2023 to 2025. Each dot is one occupation; the
ringed one is this work. Exposure is task overlap, not automation or jobs lost.
19% mean task exposure (2025)
31st percentile of 427 placed occupations
+2 pts shift 2023 → 2025
| International occupation (ISCO-08) | Task exposure (2025) | Most tasks fall in |
|---|---|---|
| Wood Treaters · 7521 | 22% | Not exposed |
| Rubber Products Machine Operators · 8141 | 18% | Not exposed |
| Glass and Ceramics Plant Operators · 8181 | 17% | Not exposed |
Read the whole six-band gradient on the GenAI exposure gradient page. The crosswalk is approximate: a US occupation can map to several international ones, and the ILO scores describe the international occupation, not this exact US role.
Tasks
All 18 tasks O*NET lists for this occupation, ordered by importance. Each links to its own page with AI-exposure and observed-use detail.
- Monitor equipment operation, gauges, and panel lights to detect deviations from standards. Core · importance 4.5
- Confer with supervisors or other equipment operators to report equipment malfunctions or to resolve production problems. Core · importance 4.3
- Press and adjust controls to activate, set, and regulate equipment according to specifications. Core · importance 4.2
- Record gauge readings, test results, and shift production in log books. Core · importance 4.2
- Stop equipment and clear blockages or jams, using fingers, wire, or hand tools. Supplemental · importance 4.2
- Read and interpret work orders and instructions to determine work assignments, process specifications, and production schedules. Core · importance 4.2
- Examine or test samples of processed substances, or collect samples for laboratory testing, to ensure conformance to specifications. Core · importance 4.2
- Load equipment receptacles or conveyors with material to be processed, by hand or using hoists. Supplemental · importance 4.2
- Remove products from equipment, manually or using hoists, and prepare them for storage, shipment, or additional processing. Supplemental · importance 4.1
- Calculate amounts of materials to be loaded into furnaces, adjusting amounts as necessary for specific conditions. Supplemental · importance 4.0
- Transport materials and products to and from work areas, manually or using carts, handtrucks, or hoists. Core · importance 4.0
- Melt or refine metal before casting, calculating required temperatures, and observe metal color, adjusting controls as necessary to maintain required temperatures. Supplemental · importance 4.0
- Weigh or measure specified amounts of ingredients or materials for processing, using devices such as scales and calipers. Supplemental · importance 3.9
- Direct crane operators and crew members to load vessels with materials to be processed. Supplemental · importance 3.8
- Feed fuel, such as coal and coke, into fireboxes or onto conveyors, and remove ashes from furnaces, using shovels and buckets. Supplemental · importance 3.6
- Replace worn or defective equipment parts, using hand tools. Supplemental · importance 3.5
- Clean, lubricate, and adjust equipment, using scrapers, solvents, air hoses, oil, and hand tools. Supplemental · importance 3.3
- Sprinkle chemicals on the surface of molten metal to bring impurities to surface and remove impurities, using strainers. Supplemental
Work activities
- Measure ingredients or substances to be used in production processes.
- Adjust temperature controls of ovens or other heating equipment.
- Monitor equipment operation to ensure proper functioning.
- Read work orders or other instructions to determine product specifications or materials requirements.
- Review blueprints or other instructions to determine operational methods or sequences.
- Record operational or production data.
- Confer with others to resolve production problems or equipment malfunctions.
- Test chemical or physical characteristics of materials or products.
- Clean production equipment.
- Maintain production or processing equipment.
- Lubricate production equipment.
- Move products, materials, or equipment between work areas.
- Clear equipment jams.
- Remove products or workpieces from production equipment.
- Calculate specific material, equipment, or labor requirements for production.
- Load materials into production equipment.
- Melt metal, plastic, or other materials to prepare for production.
- Replace worn equipment components.
- Direct operational or production activities.
Knowledge, skills & abilities
O*NET importance rating, from 1 (not important) to 5 (extremely important).
Transferable skills
| Operations Monitoring | 3.9 | |
| Operation and Control | 3.1 | |
| Quality Control Analysis | 3.0 |
Abilities
| Problem Sensitivity | 3.6 | |
| Control Precision | 3.5 | |
| Oral Comprehension | 3.4 | |
| Written Comprehension | 3.4 | |
| Arm-Hand Steadiness | 3.3 | |
| Near Vision | 3.3 | |
| Speech Recognition | 3.3 | |
| Oral Expression | 3.1 | |
| Information Ordering | 3.1 | |
| Selective Attention | 3.1 | |
| Reaction Time | 3.1 | |
| Speech Clarity | 3.1 | |
| Deductive Reasoning | 3.0 | |
| Inductive Reasoning | 3.0 | |
| Perceptual Speed | 3.0 | |
| Manual Dexterity | 3.0 | |
| Multilimb Coordination | 3.0 | |
| Trunk Strength | 3.0 | |
| Written Expression | 2.9 | |
| Category Flexibility | 2.9 | |
| Static Strength | 2.9 | |
| Finger Dexterity | 2.8 | |
| Extent Flexibility | 2.8 | |
| Auditory Attention | 2.8 |
Knowledge
Essential skills
| Reading Comprehension | 3.1 | |
| Active Listening | 3.1 | |
| Speaking | 3.1 | |
| Critical Thinking | 3.1 | |
| Monitoring | 3.1 |
Skills in demand
Skills employers ask for in job postings for this occupation (Lightcast), with whether each is a common or specialized skill.
Tools & technology
| Example | Category | |
|---|---|---|
| Microsoft Excel | Spreadsheet software | Hot technology |
| Microsoft Word | Word processing software | Hot technology |
| Inventory tracking software | Inventory management software | |
| Machine operation software | Industrial control software |
Work context
How characteristic each condition is of the job, on O*NET's 1–5 context scale (higher = more present in day-to-day work). Each condition links to how it varies across all occupations.
How to get in
- Job zone
- Zone 2 — Job Zone 1-2: Very Little to Some Preparation Needed
- Education
- Usually requires a high school diploma or GED, though some occupations may not.
- Typical entry-level education
- High school diploma or equivalent · BLS, the typical path — not a requirement
- Related experience
- Some occupations may need little or no previous experience; others require several months to a year of experience. For example, landscaping and groundskeeping workers might require very little training or previous experience, while agricultural equipment operators can benefit from on-the job training.
- Preparation level
- SVP (Below 6.0) — total schooling plus on-the-job experience.
Education of current workers
Share of people in this occupation at each level of education.
| High School Diploma | 55.9% | |
| Less than a High School Diploma | 21.4% | |
| Post-Secondary Certificate | 15.9% | |
| Some College Courses | 3.9% | |
| Bachelor's Degree | 1.8% | |
| Associate's Degree (or other 2-year degree) | 1.0% |
Interests & work styles
The interests and personal qualities O*NET associates with people who do this work.
Career interests (Holland / RIASEC)
| Realistic | 6.8 | |
| Conventional | 4.4 | |
| Investigative | 2.1 | |
| Social | 1.3 |
Interest areas
Work styles
| Dependability | 3.0 | |
| Cautiousness | 2.4 | |
| Attention to Detail | 2.2 |
Wages & employment
U.S. · annual wages (BLS OEWS)
| 10th percentile | $35,010 |
| 25th percentile | $39,250 |
| Median (50th) | $47,010 |
| 75th percentile | $57,840 |
| 90th percentile | $66,190 |
| People employed | 16,160 |
Industries that employ this occupation
Where these workers are employed, by number of jobs (national, BLS OEWS). Pay shown is the occupation's national median, not industry-specific.
| Industry | Workers | National median pay |
|---|---|---|
| Manufacturing · Sector | 13,870 | $47,290 |
| Wholesale Trade · Sector | 620 | $46,960 |
| Mining, Quarrying, and Oil and Gas Extraction · Sector | 580 | $56,070 |
| Administrative and Support and Waste Management and Remediation Services · Sector | 410 | $38,170 |
| Retail Trade · Sector | 400 | $36,510 |
| Temporary Help Services · National industry | 210 | $37,480 |
| Agriculture, Forestry, Fishing and Hunting · Sector | 50 | $40,470 |
| Management of Companies and Enterprises · Sector | — | $54,170 |
| Educational Services · Sector | — | $38,190 |
Where this work is most concentrated
Industries where this occupation is far more common than in the economy as a whole. The location quotient is how many times more concentrated it is here (a value of 5 means five times its economy-wide share).
| Industry | Concentration | Workers |
|---|---|---|
| Manufacturing · Sector | 10.37× | 13,870 |
| Mining, Quarrying, and Oil and Gas Extraction · Sector | 9.65× | 580 |
| Wholesale Trade · Sector | 0.98× | 620 |
| Temporary Help Services · National industry | 0.76× | 210 |
| Administrative and Support and Waste Management and Remediation Services · Sector | 0.43× | 410 |
| Retail Trade · Sector | 0.24× | 400 |
Related occupations
Part of the Advanced Manufacturing career cluster.
- Separating, Filtering, Clarifying, Precipitating, and Still Machine Setters, Operators, and Tenders · 51-9012.00 Compare →
- Extruding, Forming, Pressing, and Compacting Machine Setters, Operators, and Tenders · 51-9041.00 Compare →
- Metal-Refining Furnace Operators and Tenders · 51-4051.00 Compare →
- Heat Treating Equipment Setters, Operators, and Tenders, Metal and Plastic · 51-4191.00 Compare →
- Cleaning, Washing, and Metal Pickling Equipment Operators and Tenders · 51-9192.00 Compare →
- Machine Feeders and Offbearers · 53-7063.00 Compare →
- Textile Bleaching and Dyeing Machine Operators and Tenders · 51-6061.00 Compare →
- Crushing, Grinding, and Polishing Machine Setters, Operators, and Tenders · 51-9021.00 Compare →
- Adhesive Bonding Machine Operators and Tenders · 51-9191.00 Compare →
- Food and Tobacco Roasting, Baking, and Drying Machine Operators and Tenders · 51-3091.00 Compare →
- Extruding and Forming Machine Setters, Operators, and Tenders, Synthetic and Glass Fibers · 51-6091.00 Compare →
- Industrial Machinery Mechanics · 49-9041.00 Compare →
Side-by-side comparisons place two occupations’ pay, preparation, skills, and AI exposure on the same page — same data, same scale, no forecast.
What you can do with this
Options the data surfaces for Furnace, Kiln, Oven, Drier, and Kettle Operators and Tenders — not advice or a forecast. Each is a real cross-link you can follow into the evidence.
Skills that travel
Capabilities this work builds that are used across many other occupations.
Adjacent roles
Occupations O*NET rates as related — the nearby moves on the map.
- Separating, Filtering, Clarifying, Precipitating, and Still Machine Setters, Operators, and Tenders Compare →
- Extruding, Forming, Pressing, and Compacting Machine Setters, Operators, and Tenders Compare →
- Metal-Refining Furnace Operators and Tenders Compare →
- Heat Treating Equipment Setters, Operators, and Tenders, Metal and Plastic Compare →
- Cleaning, Washing, and Metal Pickling Equipment Operators and Tenders Compare →
Paths in
How people typically prepare for this work.
- High school diploma or equivalent · typical entry level
- Job zone 2 — Job Zone 1-2: Very Little to Some Preparation Needed · preparation tier
Zoom out
On the global GenAI exposure gradient this work sits around the 31st percentile of 427 international occupations.
Write a report on thisheadline · factoids · citation
Furnace, Kiln, Oven, Drier, and Kettle Operators and Tenders show 24th-percentile AI task overlap — and about 1,900 annual U.S. openings
- Furnace, Kiln, Oven, Drier, and Kettle Operators and Tenders rank in the 24th percentile (Low band) for AI task overlap across U.S. occupations — a measure of how much of the work today's AI can attempt, not how much is automated.Eloundou et al. (GPTs are GPTs) + Felten AIOE
- The occupation is projected to see about 1,900 U.S. job openings per year (2024–34), counting growth and replacement — a labor-demand projection made independently of AI.BLS Employment Projections 2024–34
- BLS projects employment to be about average (+3%) from 2024 to 2034.BLS Employment Projections 2024–34
- Median annual pay is $47,010, across about 16,160 U.S. workers.BLS OEWS (May 2024)
Copy the whole kit
Furnace, Kiln, Oven, Drier, and Kettle Operators and Tenders show 24th-percentile AI task overlap — and about 1,900 annual U.S. openings • Furnace, Kiln, Oven, Drier, and Kettle Operators and Tenders rank in the 24th percentile (Low band) for AI task overlap across U.S. occupations — a measure of how much of the work today's AI can attempt, not how much is automated. (Eloundou et al. (GPTs are GPTs) + Felten AIOE) • The occupation is projected to see about 1,900 U.S. job openings per year (2024–34), counting growth and replacement — a labor-demand projection made independently of AI. (BLS Employment Projections 2024–34) • BLS projects employment to be about average (+3%) from 2024 to 2034. (BLS Employment Projections 2024–34) • Median annual pay is $47,010, across about 16,160 U.S. workers. (BLS OEWS (May 2024)) Source: Singulariki — "Furnace, Kiln, Oven, Drier, and Kettle Operators and Tenders". https://singulariki.com/roles/role-51-9051-00 Note: AI task overlap measures what today's AI can attempt, not automation, job loss, or a forecast.
AssetsShare imageMethodology & sourcesPress & newsroomThe newsroom
Every line is built only from figures this page already shows and cites. AI task overlap means what today's AI can attempt — not automation, job loss, or a forecast.
Sources for this page
Every figure above traces to a named public dataset and the exact release below — not hand-written opinion. See the full methodology for what each measure does and does not mean.
- O*NET 30.3 U.S. Department of Labor / National Center for O*NET Development
- BLS Occupational Employment and Wage Statistics (OEWS) May 2024 U.S. Bureau of Labor Statistics
- BLS Employment Projections 2024–2034 U.S. Bureau of Labor Statistics
- Microsoft “Working with AI” working-with-ai Microsoft Research
- “GPTs are GPTs” (Eloundou et al.) arXiv 2303.10130 OpenAI / academic
- AI Occupational Exposure (AIOE) Felten, Raj & Seamans academic
- ILO / Gmyrek et al. GenAI exposure gradient 2025 International Labour Organization
- IBS O*NET-SOC ↔ ISCO-08 occupation crosswalk 2022 Institute for Structural Research (IBS)
- Frey & Osborne (2013) frey-osborne-automation academic
- Dingel & Neiman (2020) dingel-neiman-workathome academic
Data compiled June 2, 2026. Figures are estimates, not advice.
Cite this page
Plain
Singulariki. "Furnace, Kiln, Oven, Drier, and Kettle Operators and Tenders." Singulariki: a source-backed encyclopedia of work. Built from O*NET 30.3; BLS Occupational Employment and Wage Statistics (OEWS) May 2024; BLS Employment Projections 2024–2034; Microsoft “Working with AI” working-with-ai; “GPTs are GPTs” (Eloundou et al.) arXiv 2303.10130; AI Occupational Exposure (AIOE) Felten, Raj & Seamans; ILO / Gmyrek et al. GenAI exposure gradient 2025; IBS O*NET-SOC ↔ ISCO-08 occupation crosswalk 2022; Frey & Osborne (2013) frey-osborne-automation; Dingel & Neiman (2020) dingel-neiman-workathome. Accessed June 7, 2026. https://singulariki.com/roles/role-51-9051-00
APA
Singulariki. (2026). Furnace, Kiln, Oven, Drier, and Kettle Operators and Tenders. Singulariki: a source-backed encyclopedia of work. Retrieved June 7, 2026, from https://singulariki.com/roles/role-51-9051-00
BibTeX
@misc{singulariki-role-51-9051-00,
title = {Furnace, Kiln, Oven, Drier, and Kettle Operators and Tenders},
author = {{Singulariki}},
year = {2026},
note = {O*NET 30.3; BLS Occupational Employment and Wage Statistics (OEWS) May 2024; BLS Employment Projections 2024–2034; Microsoft “Working with AI” working-with-ai; “GPTs are GPTs” (Eloundou et al.) arXiv 2303.10130; AI Occupational Exposure (AIOE) Felten, Raj & Seamans; ILO / Gmyrek et al. GenAI exposure gradient 2025; IBS O*NET-SOC ↔ ISCO-08 occupation crosswalk 2022; Frey & Osborne (2013) frey-osborne-automation; Dingel & Neiman (2020) dingel-neiman-workathome. Accessed June 7, 2026},
url = {https://singulariki.com/roles/role-51-9051-00}
} Citations name the underlying public dataset releases — they reflect what this page is built from, not just the URL.