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 →Occupation · SOC 33-2022.00
Enforce fire regulations, inspect forest for fire hazards, and recommend forest fire prevention or control measures. May report forest fires and weather conditions.
Also called: Fire Management Officer · Fire Prevention Technician · Forest Officer · Forest Patrolman · Fire Operations Forester · Fire Prevention Officer · Fire Technician · Forestry Patrolman · Wildfire Mitigation Specialist · Wildfire Prevention Specialist · District Ranger · Environmental Protection Fire Control Officer
Job family: Protective Service Occupations
A source-stamped Markdown brief of this occupation — paste it into an agent, or fetch
/roles/role-33-2022-00/context.md directly.
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.
The capabilities O*NET rates most important for this occupation — the human ground the work is built on.
See all skills →Independent published positions, read together — not a forecast.
26th-percentile task overlap — yet about 300 openings a year (+14.6% projected, BLS) . What exposure means →
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.
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.) Moderate | 42nd | -0.2 | |
| LLM task exposure, γ (OpenAI / Eloundou) Low | 33rd | 0.3 | |
| AI assistant applicability (Microsoft) Low | 10th | 0.0 |
OpenAI's exposure study scores tasks three ways: with a language model alone (α 0.1), with simple added tooling (β 0.2), and including AI-powered software (γ 0.3). 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.
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 0.0 · 23rd percentile among occupations · Low
Independent U.S. Bureau of Labor Statistics employment projection for 2024–2034 — a labor-market forecast, not an AI-impact forecast.
| Outlook | Growing fast · +14.6% by 2034 |
| Projected annual openings | 300 |
| Employment 2024 → 2034 | 2,900 → 3,300 |
“Annual openings” counts new jobs plus replacements for workers who leave the occupation, so it can be large even when growth is modest.
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 occupation below. Exposure here means how much of the work's tasks today's AI can attempt — task overlap, not automation, adoption, or jobs lost.
| International occupation (ISCO-08) | Task exposure (2025) | Most tasks fall in |
|---|---|---|
| Physical and Engineering Science Technicians Not Elsewhere Classified · 3119 | 26% | 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.
All 16 tasks O*NET lists for this occupation, ordered by importance. Each links to its own page with AI-exposure and observed-use detail.
Newer responsibilities O*NET has flagged as growing for this occupation.
O*NET importance rating, from 1 (not important) to 5 (extremely important).
| Critical Thinking | 4.0 | |
| Speaking | 3.8 | |
| Active Listening | 3.6 | |
| Monitoring | 3.4 | |
| Reading Comprehension | 3.1 | |
| Writing | 3.1 | |
| Learning Strategies | 3.1 |
| Oral Expression | 4.0 | |
| Problem Sensitivity | 4.0 | |
| Oral Comprehension | 3.9 | |
| Deductive Reasoning | 3.6 | |
| Near Vision | 3.6 | |
| Written Comprehension | 3.5 | |
| Inductive Reasoning | 3.5 | |
| Flexibility of Closure | 3.5 | |
| Far Vision | 3.5 | |
| Speech Clarity | 3.4 | |
| Written Expression | 3.3 | |
| Perceptual Speed | 3.3 | |
| Information Ordering | 3.1 | |
| Speed of Closure | 3.1 |
| Coordination | 3.8 | |
| Judgment and Decision Making | 3.6 | |
| Complex Problem Solving | 3.5 | |
| Instructing | 3.3 | |
| Time Management | 3.1 | |
| Management of Personnel Resources | 3.1 |
Skills employers ask for in job postings for this occupation (Lightcast), with whether each is a common or specialized skill.
Showing the top 40 of 42.
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.
What to study: Homeland Security, Law Enforcement, Firefighting and Related Protective Services , Natural Resources and Conservation . Fields of study crosswalked to this occupation (NCES CIP–SOC), not a requirement.
Share of people in this occupation at each level of education.
| High School Diploma | 31.6% | |
| Bachelor's Degree | 24.0% | |
| Associate's Degree (or other 2-year degree) | 17.9% | |
| Some College Courses | 17.5% | |
| Post-Secondary Certificate | 8.9% |
The interests and personal qualities O*NET associates with people who do this work.
| Dependability | 9.0 | |
| Attention to Detail | 8.0 | |
| Integrity | 7.0 | |
| Cautiousness | 6.0 | |
| Self-Control | 5.0 | |
| Stress Tolerance | 4.0 | |
| Perseverance | 3.0 |
| Realistic | 6.3 | |
| Conventional | 4.6 | |
| Investigative | 3.6 | |
| Enterprising | 3.4 | |
| Social | 3.2 |
| Nature/Outdoors | 6.3 | |
| Protective Service | 4.5 | |
| Physical/Manual Labor | 4.4 | |
| Transportation/Machine Operation | 3.0 |
U.S. · annual wages (BLS OEWS)
| 10th percentile | $33,590 |
| 25th percentile | $41,000 |
| Median (50th) | $52,380 |
| 75th percentile | $77,780 |
| 90th percentile | $100,450 |
| People employed | 2,780 |
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 |
|---|---|---|
| Utilities · Sector | — | $108,060 |
| Other Services (except Public Administration) · Sector | — | $50,370 |
Part of the Energy & Natural Resources and Public Service & Safety career clusters.
Side-by-side comparisons place two occupations’ pay, preparation, skills, and AI exposure on the same page — same data, same scale, no forecast.
Options the data surfaces for Forest Fire Inspectors and Prevention Specialists — not advice or a forecast. Each is a real cross-link you can follow into the evidence.
Capabilities this work builds that are used across many other occupations.
Occupations O*NET rates as related — the nearby moves on the map.
How people typically prepare for this work.
On the global GenAI exposure gradient this work sits around the 47th percentile of 427 international occupations.
Forest Fire Inspectors and Prevention Specialists show 26th-percentile AI task overlap — and about 300 annual U.S. openings
Forest Fire Inspectors and Prevention Specialists show 26th-percentile AI task overlap — and about 300 annual U.S. openings • Forest Fire Inspectors and Prevention Specialists rank in the 26th 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 300 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 growing fast (+14.6%) from 2024 to 2034. (BLS Employment Projections 2024–34) • Median annual pay is $52,380, across about 2,780 U.S. workers. (BLS OEWS (May 2024)) Source: Singulariki — "Forest Fire Inspectors and Prevention Specialists". https://singulariki.com/roles/role-33-2022-00 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.
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.
Data compiled June 2, 2026. Figures are estimates, not advice.
Singulariki. "Forest Fire Inspectors and Prevention Specialists." 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-33-2022-00
Singulariki. (2026). Forest Fire Inspectors and Prevention Specialists. Singulariki: a source-backed encyclopedia of work. Retrieved June 7, 2026, from https://singulariki.com/roles/role-33-2022-00
@misc{singulariki-role-33-2022-00,
title = {Forest Fire Inspectors and Prevention Specialists},
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-33-2022-00}
} Citations name the underlying public dataset releases — they reflect what this page is built from, not just the URL.