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Materials Engineers vs Materials Scientists

Side-by-side · O*NET · BLS · AI-exposure research · Anthropic Economic Index

A factual, source-backed comparison of Materials Engineers and Materials Scientists on the dimensions both occupations carry. Every figure is a position within an independent published dataset — not a verdict on which job is better, safer, or more “future-proof.”

Materials Engineers Materials Scientists
Median pay · BLS OEWS
$108,310
$104,160
Employment · BLS OEWS
22,770
8,330
AI exposure (percentile) · task overlap, not automation
62nd pct
78th pct

At a glance

Dimension Materials Engineers Materials Scientists
Median pay $108,310 $104,160
Employment 22,770 8,330
Employment outlook (2024–34) · BLS projection About average (+5.7%) About average (+4.9%)
Annual openings · BLS projection 1,500 600
Typical education · O*NET Most of these occupations require a four-year bachelor's degree, but some do not. Most of these occupations require a four-year bachelor's degree, but some do not.
AI exposure · published exposure studies Moderate · 62nd pct High · 78th pct
Global GenAI gradient · ILO ISCO-08 · via crosswalk 56th pct · 30% of tasks 62nd pct · 34% of tasks
Observed AI use · Anthropic Economic Index — Augmentation-leaning (49.0%)
Mostly remote-capable · Dingel–Neiman No No

Pay and employment are BLS OEWS estimates; outlook and openings are BLS 2024–2034 projections; AI exposure and observed-use figures come from separate research and reflect exposure and usage, not predictions that either job will disappear. Compare like with like.

Skills

Shared: Engineering and Technology, Chemistry, Physics, Production and Processing, Mathematics, Reading Comprehension, Active Listening, Science, Complex Problem Solving, Oral Comprehension, Written Comprehension, Oral Expression, Deductive Reasoning, Inductive Reasoning, Category Flexibility, Critical Thinking, Written Expression, Problem Sensitivity, English Language, Writing, Speaking, Mathematics, Information Ordering, Mathematical Reasoning, Near Vision, Design, Fluency of Ideas, Computers and Electronics, Originality, Active Learning, Judgment and Decision Making, Flexibility of Closure, Speech Recognition, Speech Clarity, Monitoring, Operations Analysis.

Specific to Materials Engineers

  • Perceptual Speed
  • Visualization
  • Instructing
  • Service Orientation

Specific to Materials Scientists

  • Mechanical
  • Persuasion
  • Systems Evaluation
  • Number Facility

Knowledge, skills & abilities O*NET rates as important for each occupation. “Shared” are common to both; the columns list what is distinctive to each (top by the order O*NET surfaces).

Tools & technology

Shared: Spreadsheet software , Office suite software , Presentation software , Object or component oriented development software , Data base user interface and query software , Electronic mail software , Development environment software , Word processing software , Analytical or scientific software , Internet browser software .

Specific to Materials Engineers

Specific to Materials Scientists

Full profiles

This page is a summary. See the complete source-backed profile for Materials Engineers or Materials Scientists — tasks, the full skill graph, tools, work context, preparation, wages by percentile, industries, AI exposure and the AI work map.

More comparisons

Related occupations you can place side by side on the same sourced scale.

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.

Data compiled June 2, 2026. Figures are estimates, not advice.

Cite this page
Plain

Singulariki. "Materials Engineers vs Materials Scientists." 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; Anthropic Economic Index v4 (2026-01-15) + v2 (2025-03-27); 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/compare/materials-engineers-vs-materials-scientists

APA

Singulariki. (2026). Materials Engineers vs Materials Scientists. Singulariki: a source-backed encyclopedia of work. Retrieved June 7, 2026, from https://singulariki.com/compare/materials-engineers-vs-materials-scientists

BibTeX 
@misc{singulariki-materials-engineers-vs-materials-scientists,
  title  = {Materials Engineers vs Materials Scientists},
  author = {{Singulariki}},
  year   = {2026},
  note   = {O*NET 30.3; BLS Occupational Employment and Wage Statistics (OEWS) May 2024; BLS Employment Projections 2024–2034; Anthropic Economic Index v4 (2026-01-15) + v2 (2025-03-27); 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/compare/materials-engineers-vs-materials-scientists}
}

Citations name the underlying public dataset releases — they reflect what this page is built from, not just the URL.