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Materials Scientists vs Materials Engineers

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

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

At a glance

Dimension Materials Scientists Materials Engineers
Median pay $104,160 $108,310
Employment 8,330 22,770
Employment outlook (2024–34) · BLS projection About average (+4.9%) About average (+5.7%)
Annual openings · BLS projection 600 1,500
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 High · 78th pct Moderate · 62nd pct
Global GenAI gradient · ILO ISCO-08 · via crosswalk 62nd pct · 34% of tasks 56th pct · 30% 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, Mathematics, Reading Comprehension, Active Listening, Science, Critical Thinking, Complex Problem Solving, Oral Comprehension, Written Comprehension, Oral Expression, Written Expression, Problem Sensitivity, Deductive Reasoning, Inductive Reasoning, Writing, Information Ordering, Category Flexibility, Speaking, Active Learning, Computers and Electronics, Near Vision, Production and Processing, Design, Judgment and Decision Making, Mathematics, English Language, Fluency of Ideas, Originality, Mathematical Reasoning, Flexibility of Closure, Monitoring, Operations Analysis, Speech Recognition, Speech Clarity.

Specific to Materials Scientists

  • Mechanical
  • Persuasion
  • Systems Evaluation
  • Number Facility

Specific to Materials Engineers

  • Perceptual Speed
  • Visualization
  • Instructing
  • Service Orientation

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 , Analytical or scientific software , Word processing software , Electronic mail software , Data base user interface and query software , Development environment software , Internet browser software .

Specific to Materials Scientists

Specific to Materials Engineers

Full profiles

This page is a summary. See the complete source-backed profile for Materials Scientists or Materials Engineers — 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 Scientists vs Materials Engineers." 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-scientists-vs-materials-engineers

APA

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

BibTeX 
@misc{singulariki-materials-scientists-vs-materials-engineers,
  title  = {Materials Scientists vs Materials Engineers},
  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-scientists-vs-materials-engineers}
}

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