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Materials engineers

Also called Materials scientist engineer, Metallurgical engineer, Metallurgist, Composites engineer, Polymer engineer

In brief

In May 2025, BLS counted 22,770 US materials engineers, with median pay of $112,860 a year. Of the BLS counts since 2021, the highest was 24,630, in May 2023. The earliest figure here is an estimate of 2,500 from 1900, not measured the same way as today's count. The last full technology era for materials engineers: Computational materials science, 2005 to 2020. High-throughput DFT screening compressed the initial candidate selection phase of materials discovery, reducing the number of physical synthesis experiments required for a given property target.

What the sources say

Official outlook

BLS projects employment to grow 7.5% 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). Materials engineers: jobs and pay since 1900 [Data set; data as of BLS OEWS May 2025; BLS Employment Projections 2025-35]. https://futurehistory.earth/timeline/17-2131

Chicago

Future History. "Materials 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/17-2131.

BibTeX

@misc{futurehistory_17_2131, author = {{Future History}}, title = {Materials engineers: jobs and pay since 1900}, year = {2026}, month = oct, howpublished = {\url{https://futurehistory.earth/timeline/17-2131}}, 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.

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187519001925195019752000now
Country
2026
Known then as Mining and Metallurgical Engineer (AIME era, pre-polymer)
Known then as Metallurgical Engineer (AIME renamed, metals-dominant era)
Known then as Metallurgical and Materials Engineer (Northwestern broadening era)
Known then as Materials Engineer / Materials Scientist-Engineer (semiconductor and composites expansion)
Known then as Materials Engineers (BLS SOC 17-2131, current classification)
Known today as Materials Engineers (BLS SOC 17-2131, current classification)
Projected to still be called Materials Engineers (BLS SOC 17-2131, current classification)
Data from the May 2025 survey
US Employment
23K
Est.

Stated in source: BLS OEWS

Official statistic
Report an error
Median Annual Wage
$112,860
Est.

≈ $109,966 in 2024 prices (derived by us, CPI-adjusted)

Official statistic
Report an error
Beat · 1871

February 16, 1871: Twenty-two mining engineers found the American Institute of Mining Engineers (AIME) in Wilkes-Barre, Pennsylvania. The AIME is one of five original US national engineering societies (with ASCE, ASME, IEEE, and AIChE) and is the direct institutional predecessor of the professional materials engineer. The founding marks the first formal claim that metal and mineral engineering is a distinct learned profession requiring systematic education and peer accountability.

Beat · 1919

AIME adds "Metallurgical" to its name after the American Institute of Metals merges into the organization in 1918. This is the formal institutional recognition that materials engineering has outgrown its mining-extraction origins and now encompasses the full chain from raw material to finished alloy. The Minerals, Metals and Materials Society (TMS), one of AIME's four successor member societies, continues this tradition today.

Beat · 1955

Northwestern University's Graduate Department of Metallurgy becomes the first Department of Materials Science in the world, replacing a metals-only frame with a unified approach covering all material classes: metals, ceramics, and polymers. The ARPA Interdisciplinary Laboratory program, launched five years later in 1960, would model its approach on the Northwestern template and seed eleven more such departments at universities including MIT, Stanford, Harvard, and Cornell. This is the birth of the materials engineer as a distinct professional class with an interdisciplinary identity rather than a specialty within mining, chemistry, or physics.

Beat · 1960

July 11, 1960: ARPA announces the first three Interdisciplinary Laboratory contracts for materials research to Cornell University, the University of Pennsylvania, and Northwestern University. By 1962, nine more contracts had been awarded, seeding twelve materials research centers at major research universities. A National Academy of Sciences assessment concluded that these center-based programs were "instrumental in defining materials science and engineering as a new interdisciplinary activity." The IDL program is the pivotal federal investment that created the modern materials engineering profession.

Beat · 2011

June 24, 2011: President Obama announces the Materials Genome Initiative (MGI) with more than $500 million in federal agency commitments (DOE, DoD, NSF, NIST, NASA). The MGI's explicit goal is to cut the time to market for new materials from the historical 20+ years to under 10 years by integrating computation, experiment, and data infrastructure. The initiative repositions the materials engineer's role: no longer the expert who manually executes every experiment, but the practitioner who defines the search objective, governs the prediction-experiment cycle, and validates AI-shortlisted candidates for application deployment.

Beat · 2023

November 29, 2023: DeepMind publishes GNoME (Graph Networks for Materials Exploration) in Nature, reporting the prediction of 2.2 million stable inorganic crystal structures, more than the total compiled by human researchers over the prior two centuries. 381,000 of the most promising structures are made available to researchers worldwide; 736 of the GNoME-predicted structures have already been independently synthesized by external laboratories. Materials engineers now confront a screening space that is orders of magnitude larger than the catalog their training prepared them to navigate; the skill of picking what to synthesize from an AI-generated shortlist of millions of candidates is becoming the central practical challenge of the profession.

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 materials 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
+7.5%
BLS National Employment Matrix, 2025-35 cycle, line for SOC 17-2131 (Materials engineers): 23.8 thousand jobs in 2025, projected 25.6 thousand in 2035, a change of +1.8 thousand (+7.5%). Matrix employment counts all jobs in the occupation, including self-employed workers (3.7% 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)
31.7%
of tasks
What this measures

Published occupation-level exposure from the paper's occ_level.csv for O*NET-SOC 17-2131.00 Materials Engineers. Shown: human-rated β = 0.317, 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) 0.146 and γ (all LLM-assisted) 0.488; GPT-4-rated β 0.488. 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 materials engineers

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

AI is sitting alongside you hereScreen candidate novel crystal structures and inorganic compounds for target properties using the Materials Project database and ML interatomic potentials (M3GNet, CHGNet, MACE): query the ~160,000-entry computed database for compounds with target band gaps, bulk moduli, formation energies, or ionic conductivities

Screen candidate novel crystal structures and inorganic compounds for target properties using the Materials Project database and ML interatomic potentials (M3GNet, CHGNet, MACE): query the ~160,000-entry computed database for compounds with target band gaps, bulk moduli, formation energies, or ionic conductivities; run ML potential-accelerated molecular dynamics to compute finite-temperature stability and diffusivity for battery electrolyte or thermoelectric candidate materials at DFT accuracy and 1000x DFT speed; prioritize the top 20–50 candidates for experimental synthesis from an AI-prescreened space of millions of hypothetical structures including the GNoME library.[6],[4]

Where your edge is

ML interatomic potentials trained on DFT datasets generalize well for ground-state properties of well-represented chemical systems but can fail silently for compositions outside their training distribution, particularly for systems with unusual oxidation states, heavy elements, or strongly correlated electrons. Before committing to physical synthesis of an ML-screened candidate, cross-check the predicted structure against DFT validation for at least the top candidates in your shortlist. Flag any candidate where the ML potential predicts unusual coordination environments or where the composition class is under-represented in the Materials Project training set.

AI is sitting alongside you hereRun closed-loop AI composition optimization campaigns using Citrine Platform: define the composition design space (element ranges, process variable bounds), specify target property objectives (tensile strength, fatigue life, corrosion resistance, thermal conductivity), and configure Bayesian optimization to recommend experiment sequences that maximally reduce property uncertainty

Run closed-loop AI composition optimization campaigns using Citrine Platform: define the composition design space (element ranges, process variable bounds), specify target property objectives (tensile strength, fatigue life, corrosion resistance, thermal conductivity), and configure Bayesian optimization to recommend experiment sequences that maximally reduce property uncertainty; validate AI-recommended compositions via physical synthesis and characterization; feed experimental results back into the Citrine model to close the learning loop and converge on high-performing alloy, polymer, or ceramic formulations in 10–30% fewer experiments than traditional OFAT approaches.[5],[1]

Tools picking this up
Where your edge is

Citrine Bayesian optimization is constrained to the design space and property targets the engineer specifies, if those constraints miss a critical application requirement (creep at elevated temperature, chemical compatibility with process fluids, regulatory allowable-stress limits), the optimizer will converge on compositions that are formally optimal but fail in service. Build a rigorous requirements-capture discipline before initializing any Citrine campaign: cross-check property objectives against the full application load case, not just the primary optimization metric. Verify that allowable values are consistent with the relevant ASME, MIL-HDBK, or ASTM standards for the application.

AI is sitting alongside you herePerform cloud-scale DFT and AI-accelerated quantum chemistry calculations for novel semiconductor and quantum materials discovery using Microsoft Azure Quantum Elements: run automated high-throughput DFT workflows for hundreds of candidate compositions in parallel

Perform cloud-scale DFT and AI-accelerated quantum chemistry calculations for novel semiconductor and quantum materials discovery using Microsoft Azure Quantum Elements: run automated high-throughput DFT workflows for hundreds of candidate compositions in parallel; use AI-accelerated electronic structure methods to predict band structures, carrier mobilities, defect formation energies, and surface work functions for wide-bandgap semiconductor materials (GaN, SiC, Ga2O3, diamond) and new 2D materials (MXenes, transition metal dichalcogenides); prioritize synthesis candidates for MBE or CVD growth experiments based on computed figure-of-merit rankings.[12],[6]

Where your edge is

Azure Quantum Elements and DFT workflows provide ground-state computed properties under ideal crystal conditions: defect concentrations, surface reconstructions, grain boundary chemistry, and substrate epitaxy effects that dominate real semiconductor device performance are not captured in bulk DFT calculations. Build a systematic materials characterization gap-analysis practice: for each DFT-predicted candidate advanced to synthesis, explicitly map which properties are DFT-computable with acceptable accuracy versus which require experimental measurement (Hall mobility, minority carrier lifetime, Schottky barrier height on the target substrate), and plan the experimental characterization sequence before committing to growth runs.

Where materials 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 Materials Engineers who develop cross-functional program leadership, supplier management, and materials strategy skills are well-positioned to move into Engineering Manager roles, particularly in aerospace, automotive, semiconductor, and battery manufacturing sectors where materials decisions are on the critical path of product development programs. This transition is especially valuable now as organizations need managers who can evaluate and govern AI-assisted materials development tooling, deciding which Citrine, Schrödinger, or Granta MI deployments to invest in, setting standards for AI-predicted property claims, and managing the integration of materials informatics into product development workflows. BLS projects sustained demand driven by semiconductor fab buildouts, EV battery manufacturing scale-up, and aerospace modernization programs.

What you'd add
  • · Materials program management: managing materials development programs with stage-gate reviews, allowables database build timelines, qualification test plans, and supply chain qualification milestones
  • · AI tool evaluation and governance for materials: building review standards for AI-predicted property claims, setting experimental validation requirements for ML-shortlisted candidates before production specification
  • · Supplier and qualification management: RFQ processes for material suppliers, supplier qualification audits, and managing approved materials lists (AML) for regulated industries (aerospace, medical devices)
  • · People management in technical R&D: managing mixed teams of materials scientists, engineers, and technicians; performance reviews; career development for deep-specialist technical staff
  • · IP strategy for materials inventions: patent drafting for alloy compositions, formulations, and process inventions; licensing term negotiation with material suppliers; freedom-to-operate assessment
What it takesSome new skills to pick up
Tools of the era

How has the work of materials engineers changed since 1871?

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

  • Empirical metallurgy: optical microscope, phase diagrams, and fire assay

    The foundational tools of 19th-century materials engineers were the optical metallurgical microscope, developed by Henry Clifton Sorby in the 1860s, and the iron-carbon phase diagram, published in systematic form by Roberts-Austen in 1897. A metallurgical engineer of 1890 characterized alloys by polishing, etching with nitric acid, and examining under a metallograph; measured composition by wet chemical methods and fire assay; and designed heat treatments guided by the phase diagram. The entire knowledge base was in tables, diagrams, and physical intuition. X-ray diffraction (von Laue, 1912; Bragg, 1913) began transforming crystal structure characterization in the 1910s, but routine application in industrial labs came slowly. The pace of discovery was one experiment per day, one alloy system per career.

    Effect on the work

    The optical microscope and phase diagram approach defined the materials engineer's core skill set for over six decades and remained the primary training for industrial metallurgists well into the 1960s.

  • X-ray diffraction, electron microscopy, and WWII materials science expansion

    X-ray diffraction became routine in industrial materials labs in the 1930s, allowing non-destructive phase identification and crystal structure determination that previously required destructive chemical analysis. The transmission electron microscope, developed by Ruska and Knoll in 1933 and commercialized in the 1940s, revealed dislocation structures and nanoscale defects invisible to the optical metallograph. World War II acted as the great accelerator: rubber shortages drove synthetic polymer development; aluminum alloy design for aircraft demanded quantitative structure-property relationships; the Liberty ship hull-cracking crisis forced systematic fracture mechanics work that became the discipline of structural materials. By 1945, wartime materials laboratories had established the template of the modern interdisciplinary materials engineer who moved freely across metals, polymers, and ceramics.

    Effect on the work

    WWII and its aftermath roughly doubled the trained materials engineering workforce and institutionalized federal funding for materials research at universities, creating the pipeline that the ARPA Interdisciplinary Laboratory program would scale in 1960.

  • ARPA Interdisciplinary Laboratories, scanning electron microscopy, and semiconductor materials

    On July 11, 1960, ARPA announced the first three Interdisciplinary Laboratory contracts to Cornell University, the University of Pennsylvania, and Northwestern University, eventually expanding to twelve universities. These labs were the institutional event that created "materials science" as a named discipline: for the first time, metallurgists, ceramicists, polymer chemists, and solid-state physicists worked in the same building on the same problems. The scanning electron microscope, commercially available from Cambridge Instruments by 1965, gave materials engineers nanoscale surface imaging that transformed failure analysis and thin-film characterization. Simultaneously, the silicon transistor and integrated circuit revolution of the 1960s-1970s created an entirely new branch of materials engineering focused on semiconductor crystal growth, dopant diffusion, oxidation kinetics, and thin-film deposition. By 1975 Silicon Valley had as many materials engineers as the Pittsburgh steel district.

    Effect on the work

    The ARPA IDL program is credited by the National Academy of Sciences with defining materials science and engineering as an interdisciplinary field, triggering a rapid increase in materials science departments between 1960 and 1970, and establishing the professional identity of the materials engineer as distinct from the metallurgist or ceramicist.

  • CAD/FEA integration, EDS/EBSD characterization, and composites engineering

    The 1980s brought two simultaneous expansions to the materials engineer's toolkit. First, finite element analysis software (NASTRAN, ANSYS, ABAQUS) matured to the point where materials engineers could model stress distributions in complex geometries, coupling materials property inputs to structural response and iterating on material selection in software rather than through destructive physical testing. Second, energy-dispersive X-ray spectroscopy (EDS) and, later, electron backscatter diffraction (EBSD) in the SEM transformed microstructure characterization from hours of tedious manual measurement into automated phase identification and grain mapping. Carbon fiber reinforced polymer (CFRP) composites, which had been primarily aerospace research materials in the 1970s, became production materials for Boeing and Airbus in the 1980s, creating a new composites engineering specialization requiring materials engineers who understood fiber-matrix interfaces, layup sequences, and autoclave cure kinetics. The Metals Handbook (ASM International) grew to seventeen volumes by 1990, encoding the accumulated empirical knowledge of the profession.

    Effect on the work

    FEA integration accelerated design cycles and reduced the need for extensive physical prototype testing, modestly compressing some experimental iterations. The composites expansion and semiconductor growth more than offset any FEA-driven efficiency gains in employment terms.

  • Computational materials science: DFT, CALPHAD, and high-throughput screening

    Density functional theory (DFT) computational methods, accelerated by the growth of Linux HPC clusters and codes like VASP (Vienna Ab-initio Simulation Package) and Quantum ESPRESSO, began moving from academic research into industrial materials engineering practice in the mid-2000s. CALPHAD thermodynamic modeling tools (Thermo-Calc, Pandat) allowed rapid calculation of phase equilibria for multi-component alloy systems that would have required months of experimental work. The Materials Project, launched by Lawrence Berkeley National Laboratory in 2011, made tens of thousands of computed materials properties freely available online; by 2015 it had grown to tens of thousands of entries and was being used by industrial materials engineers for initial composition screening. The Materials Genome Initiative, launched by President Obama in June 2011 with $500 million in federal agency commitments, formalized the shift: the human materials engineer was repositioned from bench experimenter to prediction-experiment-validation loop manager, defining search objectives and interpreting results rather than manually executing every measurement.

    Effect on the work

    High-throughput DFT screening compressed the initial candidate selection phase of materials discovery, reducing the number of physical synthesis experiments required for a given property target. Employment of materials engineers remained roughly flat during this period as expanded scope offset efficiency gains.

  • AI materials informatics: GNoME, Citrine Platform, Schrodinger, Azure Quantum Elements

    The AI wave that arrived in materials science after 2020 is categorically different from the DFT-and-CALPHAD era that preceded it. DeepMind's GNoME (Graph Networks for Materials Exploration, November 2023) predicted 2.2 million stable crystal structures in a single model run, more than the entire prior experimental catalog built over two centuries. Citrine Informatics' Platform 3.0 closes the loop between AI prediction and physical experiment using Bayesian optimization, recommending the next alloy composition or polymer formulation to test to maximally reduce property uncertainty with the fewest trials. Microsoft Azure Quantum Elements (launched 2023) runs hundreds of parallel DFT jobs in the cloud at many times the speed of local HPC clusters. Schrodinger's Materials Science suite predicts battery electrolyte ionic conductivity, semiconductor band structures, and polymer glass-transition temperatures from molecular simulation calibrated by ML potentials. The human materials engineer's value in this environment concentrates on three things that AI cannot do: defining the application-constraint search objective correctly, validating that AI-predicted compositions actually synthesize and perform as expected in real-world conditions, and signing the licensed engineering documentation that governs safety-critical materials use in aerospace, medical devices, and critical infrastructure.

    Effect on the work

    AI materials informatics tools are compressing experimental design cycles in initial candidate screening. The BLS projects employment growth for materials engineers 2024-2034, suggesting the structural demand drivers (EV batteries, advanced semiconductors, aerospace composites) outweigh any AI-driven compression of headcount. The materials engineer is not being displaced; the job content is shifting toward AI tool governance and experimental validation.

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Materials Engineers employment and pay by year

On record since1871
Latest US employment22,770 (May 2025)Source: BLS OEWS May 2025Official statisticReport an error
Latest US median pay$112,860 (May 2025)Source: BLS OEWS May 2025Official statisticReport an error
Outlook+7.5% 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 21322)

    Employment, 2020
    2,630, Statistics Canada Census 2021 Official statistic Report an errorStatistics Canada Census of Population 2021: number of persons with employment income in NOC 21322 Metallurgical and materials engineers, Canada, reference year 2020. A recipients basis (anyone with employment income during the year), slightly broader than a point-in-time employed count. Statistics Canada Open Licence.
    Median pay, 2020
    C$111,000, Statistics Canada Census 2021 Official statistic Report an errorStatistics Canada Census of Population 2021 (Table 98-10-0586): median employment income for NOC 21322 Metallurgical and materials engineers, Canada, reference year 2020, full-year full-time workers (the basis comparable to the US OEWS full-time median). Statistics Canada Open Licence.
View all 28 years of cited US data, each linked to its source
YearUS employmentAnnual pay (median unless marked)SourceReport
19002,500n/aEst., NBER Researched contextReport
19308,000n/aDecennial Census 1930 Researched contextReport
195014,000$4,200Est., NBER (employment) Researched contextEst., University of Missouri Libraries (pay) Researched contextReport
197518,000n/aEst., BLS FRASER Researched contextReport
199021,000$45,000BLS CPS March 1990 Researched contextReport
200323,120$62,930BLS OEWS May 2003 Official statisticReport
200421,130$67,110BLS OEWS May 2004 Official statisticReport
200520,950$69,660BLS OEWS May 2005 Official statisticReport
200621,230$73,990BLS OEWS May 2006 Official statisticReport
200721,910$77,170BLS OEWS May 2007 Official statisticReport
200824,160$81,820BLS OEWS May 2008 Official statisticReport
200922,510$83,190BLS OEWS May 2009 Official statisticReport
201021,830$83,120BLS OEWS May 2010 Official statisticReport
201122,160$84,550BLS OEWS May 2011 Official statisticReport
201222,740$85,150BLS OEWS May 2012 Official statisticReport
201324,190$87,330BLS OEWS May 2013 Official statisticReport
201424,990$87,690BLS OEWS May 2014 Official statisticReport
201527,040$91,310BLS OEWS May 2015 Official statisticReport
201626,800$93,310BLS OEWS May 2016 Official statisticReport
201727,200$94,610BLS OEWS May 2017 Official statisticReport
201826,930$92,390BLS OEWS May 2018 Official statisticReport
201926,820$93,360BLS OEWS May 2019 Official statisticReport
202024,740$95,640BLS OEWS May 2020 Official statisticReport
202121,530$98,300BLS OEWS May 2021 Official statisticReport
202221,510$100,140BLS OEWS May 2022 Official statisticReport
202324,630$104,100BLS OEWS May 2023 Official statisticReport
202422,770$108,310BLS OEWS May 2024 Official statisticReport
202522,770$112,860BLS OEWS May 2025 Official statisticReport
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