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Mining engineers

Official title Mining and Geological Engineers, Including Mining Safety Engineers (SOC 17-2151)

Also called Geological engineer, Mine engineer, Mineral engineer, Pit engineer, Mining safety engineer

In brief

In May 2025, BLS counted 6,080 US mining engineers, with median pay of $106,220 a year. Of the BLS counts since 2021, the highest was 7,390, in May 2022. The earliest figure here is a Census count of 303 from 1900, not measured the same way as today's count. The last full technology era for mining engineers: Autonomous haul trucks, 2008 to 2020. Autonomous haulage displaced haul truck operators (a non-engineering occupation) and restructured the engineering role toward system governance.

What the sources say

Official outlook

BLS projects employment to grow 3.8% from 2025 to 2035.

BLS Employment Projections 2025-35

AI exposure studies

Data as of BLS OEWS May 2025; BLS Employment Projections 2025-35. BLS publishes these counts as yearly survey snapshots and cautions against reading them as a precise trend.

Last updated .

Cite this page
APA

Future History. (2026, October 11). Mining engineers: jobs and pay since 1900 [Data set; data as of BLS OEWS May 2025; BLS Employment Projections 2025-35]. https://futurehistory.earth/timeline/17-2151

Chicago

Future History. "Mining engineers: jobs and pay since 1900." Data as of BLS OEWS May 2025; BLS Employment Projections 2025-35. Last modified October 11, 2026. https://futurehistory.earth/timeline/17-2151.

BibTeX

@misc{futurehistory_17_2151, author = {{Future History}}, title = {Mining engineers: jobs and pay since 1900}, year = {2026}, month = oct, howpublished = {\url{https://futurehistory.earth/timeline/17-2151}}, note = {Last updated 2026-10-11. Data as of BLS OEWS May 2025; BLS Employment Projections 2025-35}, }

The access date is today's date on your device.

drag to travel through time
187519001925195019752000now
Country
2026
Known then as Mining Engineer / Mine Agent (pre-professional era, apprenticeship and self-taught)
Known then as Mining Engineer (AIME-credentialed professional era)
Known then as Mining Engineer / Mineral Engineer (university-degree era, SME formation)
Known then as Mining Engineer / Mining Safety Engineer (MSHA-era, safety specialization)
Known then as Mining and Geological Engineers, Including Mining Safety Engineers (BLS SOC 17-2151)
Known today as Mining and Geological Engineers, Including Mining Safety Engineers (BLS SOC 17-2151)
Projected to still be called Mining and Geological Engineers, Including Mining Safety Engineers (BLS SOC 17-2151)
Data from the May 2025 survey
US Employment
6K

Stated in source: BLS OEWS

Official statistic
Report an error
Median Annual Wage
$106,220
Est.

≈ $103,497 in 2024 prices (derived by us, CPI-adjusted)

Official statistic
Report an error
Beat · 1864

Columbia University's School of Mines opens on November 15, 1864 in New York City -- the first mining and metallurgy school in the United States. Twenty-four students enroll in the inaugural class. The school is founded in part to address the urgent wartime need for domestically trained engineers to supply coal, iron, and copper for the Union war effort.

Beat · 1871

The American Institute of Mining Engineers (AIME) is founded on February 16, 1871 by 22 mining engineers gathering in Wilkes-Barre, Pennsylvania. This is the institutional birth of the mining engineer as a recognized profession in the United States. By the end of the century membership exceeds 1,000; by 1915 it exceeds 5,000.

Beat · 1907

The Monongah mine disaster (December 6, 1907, West Virginia) kills at least 362 men in the worst coal mine accident in US history. Congress creates the US Bureau of Mines in 1910 in direct response, imposing engineering accountability standards on mine safety design and ventilation. The disaster is a turning point in establishing that mine engineering requires formal technical oversight, not just experienced labor.

Beat · 1977

The Federal Mine Safety and Health Act of 1977 (Public Law 95-164) creates MSHA and for the first time imposes personal legal accountability on the responsible engineer for ground-control plans, ventilation surveys, and safety documentation across all US mines. This regulatory framework becomes the durable moat that protects the licensed mining engineer from displacement by automation: no software output can substitute for a PE-certified signature under 30 CFR.

Beat · 2023

KoBold Metals announces the discovery of a large copper deposit in Zambia's Copperbelt in partnership with BHP, the first major copper discovery found primarily through machine-learning guided exploration. The announcement marks a shift in the geochemical and geophysical exploration workflow: ML-ranked drill targets are entering standard practice at junior and senior mining companies, requiring geological engineers to integrate AI output into their targeting decisions.

How to read this number

OEWS is a point-in-time survey snapshot, not a continuous time series; BLS advises against using it for year-over-year trend comparison.

Each dot is a cited figure over time; the dotted line only links them (values between aren't measured). Hollow dots are estimates.

Official statistic: Copied by a script from a statistics agency table or a published study's table, not retyped by hand. BLS figures and the forecast and exposure tables are also re-checked against that table on every data run. Researched context: Researched from the cited source rather than copied from it by a script, so it is not machine-checked yet. Spot a mistake? Each figure has a “Report an error” link.

Cited forecasts · present → 2035

Will AI replace mining engineers? What the research says

Each figure is stated by the named source; we publish no forecasts of our own. Where sources disagree, we show the disagreement rather than an average.

Each figure is stated by the named source; we publish no forecasts of our own. Where sources disagree, the spread is the honest signal.

What published forecasts say
Forecast change in the number of people doing this work, as published by each named source. Bars share one scale on every role, centred on zero and reaching the end at ±30%. Grey marks a small change, 3% or less either way.
BLS Employment Projections 2025-35
→ 2035
+3.8%
BLS National Employment Matrix, 2025-35 cycle, line for SOC 17-2151 (Mining and geological engineers, including mining safety engineers): 6.2 thousand jobs in 2025, projected 6.4 thousand in 2035, a change of +0.2 thousand (+3.8%). Matrix employment counts all jobs in the occupation, including self-employed workers (1.6% of this occupation in 2025). The average for all occupations in this cycle is +3.5%.
Official statisticReport an error
How exposed is this work to AI (research measures)
Scores as published by each study. Each row says what it measures. These measure what AI could technically do, not forecasts of jobs lost.
Eloundou et al. (2023), "GPTs are GPTs"
2023 study · Share of tasks exposed to LLMs (β, human-rated)
50%
of tasks
What this measures

Published occupation-level exposure from the paper's occ_level.csv for O*NET-SOC 17-2151.00 Mining and Geological Engineers, Including Mining Safety Engineers. Shown: human-rated β = 0.5, the share of the occupation's tasks an LLM could do in at least half the time at equal quality, counting tasks that need LLM-powered software at half weight (β = E1 + 0.5 × E2). Other published measures for this occupation: human-rated α (LLM alone) 0.407 and γ (all LLM-assisted) 0.593; GPT-4-rated β 0.481. This measures how much of the work an LLM could touch, not how many jobs disappear.

Official statisticReport an error
Today, in this role

What's changing day to day for mining engineers

Three parts of your work where AI is already doing real lifting, and what stays yours.

AI is sitting alongside you hereBuild 3D geological ore body models using Seequent Leapfrog Geo with ML-assisted implicit modelling: import drill-hole assay and lithology data

Build 3D geological ore body models using Seequent Leapfrog Geo with ML-assisted implicit modelling: import drill-hole assay and lithology data; apply Leapfrog's implicit interpolation algorithms and ML-assisted vein interpretation to build grade-shell and lithological envelope models; compare AI-interpolated grade distribution against geostatistical variogram models to validate the implicit model reproduces geological continuity correctly; sign off on the ore body model as the basis for resource estimation before submission to a Qualified Person (QP) for NI 43-101 or JORC reporting.[8],[1]

Where your edge is

Leapfrog Geo's implicit modelling dramatically compresses model-build time from weeks to days, but ML-interpolated grade shells can create geologically implausible continuity in areas of sparse drilling or complex structural geology. Before sign-off, cross-validate the AI-interpolated model against cross-sections oriented along the mineralization plunge direction, check that high-grade corridors respect interpreted structural controls (faults, fold limbs), and flag any volumes where the drill-hole spacing exceeds the variogram range: these are the areas where the implicit model is extrapolating without data support and where the QP report may require additional uncertainty disclosure.

AI is sitting alongside you hereCommission and supervise daily drone-based pit surveying campaigns using Propeller Aero or Skycatch platforms: define drone flight patterns and GCP (ground control point) placement to achieve survey-grade accuracy (±50 mm) across the active pit benches

Commission and supervise daily drone-based pit surveying campaigns using Propeller Aero or Skycatch platforms: define drone flight patterns and GCP (ground control point) placement to achieve survey-grade accuracy (±50 mm) across the active pit benches; review AI-generated stockpile volume calculations and bench face advance rate metrics against the mine plan; investigate discrepancies between drone-measured ore tonnes moved and ROM pad weightometer data as indicators of density assumption errors or ore loss; generate daily mine call factor reports from the drone survey data for reconciliation against the geological block model.[10],[1]

Tools picking this up
Where your edge is

Propeller Aero automates volumetric calculations from drone point clouds with documented accuracy, but mine call factor reconciliation, comparing drone-surveyed movement with mill throughput and grade, requires the engineer to diagnose systematic errors that persist across multiple survey cycles. The drone survey data quantifies the discrepancy but the engineering investigation requires integrating drill-hole grade data, density measurement protocols, and plant-feed sampling results to isolate the cause.

AI is sitting alongside you hereDevelop and optimize open-pit and underground mine production schedules using Deswik or Maptek Vulcan scheduling modules with AI-assisted optimization: define material movement constraints (truck fleet capacity, crusher throughput, stope cycle times, paste fill delivery rate)

Develop and optimize open-pit and underground mine production schedules using Deswik or Maptek Vulcan scheduling modules with AI-assisted optimization: define material movement constraints (truck fleet capacity, crusher throughput, stope cycle times, paste fill delivery rate); run AI-driven scenario optimization to sequence thousands of mining blocks or underground stopes to maximize NPV while respecting grade blending targets, equipment utilization, and ROM pad capacity; compare AI-optimized schedules against manual sequences to verify that AI recommendations honour practical mining constraints (advance rate limits, blast clearance times, pillar sequencing requirements).[9],[1]

Where your edge is

AI scheduling optimization maximizes NPV mathematically but cannot independently account for operational constraints that are not encoded in the model: weather-related access limitations, union agreement restrictions on operating hours, unmodelled ground support requirements in weak ground areas, or equipment availability patterns that differ from fleet-average assumptions. Before locking an AI-optimized schedule, workshop it with the mine operations supervisor and geotechnical team to surface constraint violations the optimizer missed; maintain a standing register of "model limitations" that get reviewed every time the schedule is rerun.

Where mining engineers go next

Natural next steps for someone with your foundation: not exits, evolutions.

What each move takes

A direction you could grow

Architectural and Engineering Managers

Senior mining engineers who develop cross-functional program leadership, autonomous-fleet governance, and digital-mining transformation skills are well-positioned to move into Engineering Manager roles overseeing mine digitalization programs. Mining organizations are urgently seeking engineering leaders who can credibly evaluate autonomous equipment programs (Caterpillar Command, Komatsu FrontRunner), govern AI scheduling tools, and translate ML exploration results into capital allocation decisions. The market for digital-mining transformation leaders is accelerating: Rio Tinto's Centre of Excellence for automation and AI, BHP's OneMine digital program, and Newmont's Full Potential transformation initiative all create Engineering Manager roles that require the combination of deep mining engineering knowledge and AI governance judgment that practicing mining engineers uniquely possess.

What you'd add
  • · Digital mining program management: building autonomous haulage system (AHS) commissioning programs, managing staged ODD expansion, and establishing safety-governance frameworks for AI-controlled equipment fleets
  • · Technology evaluation and vendor management: assessing AHS, AI scheduling, and predictive maintenance platforms for fit with specific mine conditions; negotiating OEM technology agreements and managing performance SLAs
  • · Mine operations financial management: mine operating cost budgeting, capital expenditure proposal development for autonomous fleet investment, NPV analysis of digital mining initiatives
  • · People leadership in a digitally transforming mining workforce: managing transitions from operator-intensive to engineer-governed autonomous operations; change management for MSHA-regulated workforces
  • · Executive communication: translating AHS safety performance metrics, AI scheduling NPV improvements, and ML exploration results into capital allocation decisions and board-level reporting for mining company leadership
What it takesSome new skills to pick up
Tools of the era

How has the work of mining engineers changed since 1864?

The tools that defined the work, one line per era. Select an era to see how it reshaped the work.

  • Transit, level, and hand survey instruments (classical mine surveying era)

    The founding tool of the mining engineer was the surveyor's transit: a theodolite-based optical instrument for measuring angles and plotting underground workings on paper. Mine mapping in the nineteenth century was entirely analog, done by the engineer with a compass, steel tape, and plumb bob in a mine heading lit by candles or oil lamps. The output was a hand-drawn mine plan, maintained on linen or paper, that showed the development headings, ore body outlines, shaft positions, and ventilation arrangements. The quality of the mine plan directly governed the safety and efficiency of the operation: an inaccurate plan could result in a heading cutting into an old flooded working and causing a catastrophic inrush. This era also encompassed the first blasting explosives used routinely in mining: black powder in use since the 1600s was joined by dynamite (Alfred Nobel, 1867) and gelignite (1875), which gave engineers the ability to break hard rock efficiently for the first time.

    Effect on the work

    In this era the mining engineer was irreplaceable: no mechanized substitute existed for field measurement, underground observation, and map-making judgment. The profession was small and highly paid relative to mining labor.

  • Rock drills, mechanical coal cutters, and early ventilation fans (mechanized underground era)

    The Sullivan rock drill (pneumatic, widely adopted after 1880) and the Jeffrey coal cutting machine transformed underground mine development from a hand-labor process to a mechanized one. For the mining engineer, mechanization increased the technical complexity of the work: the engineer now had to specify compressed-air supply systems, fan sizing, and drill-hole patterns for blasting in addition to the survey and map functions of the previous era. The BLS Wage Chronology for bituminous coal (1933) documents the formalization of safety engineering responsibilities: after the US Bureau of Mines was established in 1910 following the Monongah (1907) and Cherry Mine (1909) disasters, engineers became legally accountable for ventilation design and mine safety plans. The 1910s-1930s also saw the first geostatistical methods for ore reserve estimation enter practice, giving geological engineers a quantitative framework for resource reporting that would eventually evolve into the modern NI 43-101 and JORC standards.

    Effect on the work

    Mechanization of coal cutting and development drilling did not displace the mining engineer; it expanded the engineer's responsibilities to include supervision of mechanical systems. Employment of mining engineers grew steadily through the 1920s peak.

  • Open-pit mechanization: electric shovels, large-diameter blast drills, and haul trucks (Bingham Canyon model)

    The post-WWII decades saw the transformation of copper, iron ore, and coal production from underground dominance to open-pit predominance. Bingham Canyon in Utah became the archetype: a mine designed by engineers working with electric shovels, large-diameter rotary blast drills, and diesel haul trucks capable of moving payloads. The engineering challenge shifted: instead of underground support and ventilation, open-pit engineers had to design pit wall slopes that were stable across hundreds of meters of vertical relief, manage haul-road grades for the growing truck fleet, and calculate ore blending from multiple rock types and grades across a pit floor spanning kilometers. The FORTRAN-based pit optimization programs of the 1960s, which implemented the Lerchs-Grossmann algorithm (1965), gave engineers the first computerized tool for designing the economically optimal pit shell, a foundational innovation that all modern open-pit planning software descends from.

    Effect on the work

    Open-pit methods produced lower unit mining costs than underground methods and enabled the exploitation of lower-grade ore bodies that would have been uneconomic underground. The engineering workforce grew in absolute terms, though the ratio of engineers to mine workers fell as operations scaled.

  • MSHA regulatory framework + early CAD/CAM mine design (Federal Mine Safety and Health Act, 1977)

    The Federal Mine Safety and Health Act of 1977 created the Mine Safety and Health Administration (MSHA) and fundamentally restructured the mining engineer's accountability. For the first time, engineers faced personal legal liability for signed safety documents: ground-control plans, ventilation surveys, and roof support specifications submitted to MSHA under 30 CFR Parts 56, 57, and 75. The safety engineer emerged as a distinct specialization within the profession. Simultaneously, the late 1970s and 1980s brought early CAD tools into mine design: initial versions of what would become Maptek Vulcan (released 1990) and Surpac (released 1987) gave geological engineers the first software environments for 3D ore body modelling, replacing hand-drawn cross-sections. By the mid-1990s, most major mining companies had migrated from drafting-table mine planning to PC-based CAD workflows.

    Effect on the work

    MSHA's accountability framework made the PE-licensed mining engineer non-substitutable for regulated safety functions: automation could compress planning and scheduling work but could not remove the engineer's legal sign-off. This moat has deepened, not eroded, with subsequent regulation.

  • 3D geological modelling software: Leapfrog Geo, Vulcan, Surpac (implicit modelling era)

    Seequent's Leapfrog Geo, first released in 2004, introduced implicit 3D geological modelling to mining practice: instead of manually digitizing cross-sections and projecting between sections by hand, geological engineers could feed drill-hole assay and lithology data directly into an algorithm that built a continuous 3D grade shell model. The productivity improvement was dramatic, compressing ore body model builds from weeks to days. Maptek Vulcan and Datamine Studio carried parallel advances in geostatistical estimation (ordinary kriging, simulation) and pit optimization. By 2010, the engineer's daily workflow had been transformed: ore body modelling, resource estimation, pit design, and production scheduling all ran within integrated PC-based software environments that did not exist fifteen years earlier. Drone-based photogrammetric surveying entered mining practice around 2010-2012, beginning to replace traditional ground-survey campaigns for pit face mapping.

    Effect on the work

    Geological modelling software compressed the time required to build a resource model, potentially allowing one geological engineer to produce what previously required a team. At major mining companies, headcount in geological modelling teams did not fall commensurately, as the faster cycle time enabled more frequent model updates and more iterative exploration drilling programs.

  • Autonomous haul trucks: Komatsu FrontRunner AHS (2008), Caterpillar Command for Mining (2013)

    Komatsu's FrontRunner Autonomous Haulage System, deployed at Codelco's Gabriela Mistral copper mine in Chile in 2008, was the first commercial autonomous haul truck operation in the world. Rio Tinto's Mine of the Future program brought Komatsu AHS to its Pilbara iron-ore operations starting in 2012; by 2024 Rio Tinto operated more than a hundred autonomous trucks generating documented 15% productivity improvements and near-zero lost-time incidents associated with autonomous operations. Caterpillar's Command for Mining launched commercially in 2013 and deployed at BHP, Freeport-McMoRan, and Teck operations. For the mining engineer, autonomous haulage fundamentally restructured the role: the engineer ceased supervising truck operators and instead governed the autonomous system, defining geofence boundaries and operational design domains, managing exception reports, and certifying the system under MSHA's 30 CFR provisions for operator-in-control equipment.

    Effect on the work

    Autonomous haulage displaced haul truck operators (a non-engineering occupation) and restructured the engineering role toward system governance. The net effect on mining engineer headcount was positive: AHS programs require more specialized engineering oversight per operation than conventional haulage, not less.

  • ML-driven mineral exploration and AI mine planning: KoBold Metals, Deswik AI scheduling, Leapfrog ML (critical-minerals era)

    KoBold Metals, founded in 2018 and backed by Breakthrough Energy Ventures and Andreessen Horowitz, deployed machine-learning models trained on global geophysical and geochemical datasets to rank exploration targets for copper, cobalt, nickel, and lithium. The Zambia copper discovery announced in 2023 with BHP partnership demonstrated that ML-assisted exploration could locate a world-class ore body in an under-explored terrain. Deswik's AI scheduling module (2024-2025) introduced mathematical-programming optimization for open-pit bench and underground stope sequencing, enabling schedule optimization across thousands of mining blocks in minutes. Propeller Aero and Skycatch platforms automated pit volume calculations from daily drone surveys, replacing ground-survey campaigns. SparkCognition Darwin brought autonomous anomaly detection to crusher and mill equipment health monitoring. Together these tools represent the most significant reconfiguration of the mining engineer's daily workflow since the introduction of CAD in the 1980s: tasks that were previously time-consuming analytical processes can now be accomplished with AI assistance in a fraction of the time, but every output requires engineering sign-off before implementation.

    Effect on the work

    AI and automation tools compress the time required for planning, modelling, and scheduling tasks substantially, but the PE-licensed sign-off requirement on safety-critical documents, the MSHA accountability framework, and the physical field judgment required to validate AI-generated models against real mine conditions mean these tools augment engineers rather than replacing them. The critical-minerals buildout (BLS projects growth 2024-2034) is currently the dominant structural force, partially offsetting automation-driven productivity gains.

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Mining and Geological Engineers, Including Mining Safety Engineers employment and pay by year

On record since1864
Latest US employment6,080 (May 2025)Source: BLS OEWS May 2025Official statisticReport an error
Latest US median pay$106,220 (May 2025)Source: BLS OEWS May 2025Official statisticReport an error
Outlook+3.8% by 2035Source: BLS Employment Projections 2025-35Official statisticReport an error

In other countries

The latest figure each country's statistics agency publishes for this job. Each uses its own occupation definition, survey basis and currency, so the numbers are not directly comparable with each other or with the US figures.

  • Canada (NOC 21330)

    Employment, 2020
    6,975, Statistics Canada Census 2021 Official statistic Report an errorStatistics Canada Census of Population 2021: number of persons with employment income, the EXACT SUM of the cited cells for NOC 21330 + 21331 (this US occupation has no single NOC unit-group equivalent), combined as Mining and geological engineers, Canada, reference year 2020. A recipients basis (anyone with employment income during the year), slightly broader than a point-in-time employed count. A sum of cited cells, not an estimate. Statistics Canada Open Licence.
    Median pay, 2020
    C$131,000, Statistics Canada Census 2021 Official statistic Report an errorStatistics Canada Census of Population 2021 (Table 98-10-0586): median employment income for NOC 21330 Mining engineers, the dominant constituent of the combined Mining and geological engineers (NOC 21330 + 21331), Canada, reference year 2020, full-year full-time workers (the basis comparable to the US OEWS full-time median). This is the cited median of the dominant group, not a blend of the constituent medians. Statistics Canada Open Licence.
View all 28 years of cited US data, each linked to its source
YearUS employmentAnnual pay (median unless marked)SourceReport
1900303n/aDecennial Census 1943 Researched contextReport
1910428n/aDecennial Census 1943 Researched contextReport
1920572n/aDecennial Census 1943 Researched contextReport
1930534n/aDecennial Census 1943 Researched contextReport
1990n/a$44,000Est., BLS Monthly Labor Review Researched contextReport
20034,730$62,520BLS OEWS May 2003 Official statisticReport
20045,050$64,690BLS OEWS May 2004 Official statisticReport
20055,680$70,070BLS OEWS May 2005 Official statisticReport
20066,810$72,160BLS OEWS May 2006 Official statisticReport
20077,150$74,330BLS OEWS May 2007 Official statisticReport
20086,900$75,960BLS OEWS May 2008 Official statisticReport
20096,310$79,440BLS OEWS May 2009 Official statisticReport
20106,270$82,870BLS OEWS February 2012 (employment) Official statisticBLS OEWS May 2010 (pay) Official statisticReport
20116,630$84,300BLS OEWS May 2011 Official statisticReport
20127,640$84,320BLS OEWS May 2012 Official statisticReport
20137,990$86,870BLS OEWS May 2013 Official statisticReport
20148,200$90,160BLS OEWS May 2014 Official statisticReport
20158,000$94,040BLS OEWS May 2015 Official statisticReport
20166,940$93,720BLS OEWS May 2016 Official statisticReport
20176,150$94,240BLS OEWS May 2017 Official statisticReport
20185,780$92,250BLS OEWS May 2018 Official statisticReport
20196,280$91,160BLS OEWS May 2019 Official statisticReport
20206,270$93,800BLS OEWS May 2020 Official statisticReport
20217,370$97,090BLS OEWS May 2021 Official statisticReport
20227,390$97,490BLS OEWS May 2022 Official statisticReport
20237,040$100,640BLS OEWS May 2023 Official statisticReport
20246,770$101,020BLS OEWS May 2024 Official statisticReport
20256,080$106,220BLS OEWS May 2025 Official statisticReport
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