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Starting Salary
Median Salary
Top Earners
Job Growth
Professionals in USA

Career Overview

Materials engineers study the structure and properties of substances at the atomic and molecular level to develop new materials or improve existing ones. They work across industries such as aerospace, semiconductors, biomedical devices, energy, automotive, and consumer goods, selecting materials for specific applications, analyzing failures, and designing processes to manufacture materials reliably at scale.

Day to day, the job blends lab work, computer modeling, and cross-functional collaboration. Materials engineers run experiments using tools like electron microscopes and X-ray diffraction, interpret test data, write technical reports, and partner with design, manufacturing, and quality teams. As industries push toward batteries, lightweight alloys, recyclable plastics, and advanced semiconductors, the role has become central to innovation and sustainability.

Salary Range (US, estimates)

Entry level$68,000
Median$104,000
Senior$135,000
Top 10%$170,000

Key Statistics

Job growth+5%
Professionals in the USA0.03 million
Typical hours/week42 hrs
Remote work share8%
Annual job openings2,300/yr
Demand3.7 / 10 (Low)

Education Paths

  • Required minimum: Bachelor's in Materials Science & Engineering — A four-year degree in materials science, metallurgical, ceramic, or polymer engineering (or a related field like chemical or mechanical engineering) qualifies most graduates for entry-level roles in manufacturing, testing, and quality.
  • Most common: Master's Degree in Materials Science — A master's degree opens doors to R&D, specialized process engineering, and faster advancement. Many employers in semiconductors, biomedical, and aerospace prefer or reward graduate-level training, and a PhD is typically needed for research scientist and academic roles.

Certifications

  • Accelerator: PE License or Industry Certifications — A Professional Engineer (PE) license, along with credentials such as Six Sigma Green/Black Belt or ASM International certifications, strengthens credibility, supports leadership paths, and can increase earning potential.

Core Skills

  • Materials characterization (SEM, XRD, TEM, spectroscopy)
  • Thermodynamics and phase transformations
  • Failure analysis and root cause investigation
  • Computational modeling and simulation (FEA, DFT, CALPHAD)
  • Data analysis and programming (Python, MATLAB)
  • Technical writing and cross-functional communication

Pros

  • Strong job stability with demand across aerospace, energy, electronics, biomedical, and automotive industries
  • Opportunity to work on cutting-edge innovations like batteries, semiconductors, and sustainable materials
  • Good salary with solid earning potential, especially with advanced degrees or specialization
  • Varied work mixing lab experimentation, computation, and collaboration

Cons

  • Advanced degrees (MS or PhD) are often needed for research-focused and senior roles
  • Roles can be concentrated in specific geographic regions near manufacturing or research hubs
  • Projects can have long development timelines with slow, incremental progress
  • Some work involves exposure to hazardous materials, high temperatures, or demanding lab and plant environments

AI Impact on This Career

AI and machine learning are transforming materials discovery by rapidly screening compounds, predicting properties, and optimizing processing parameters. Rather than replacing materials engineers, these tools are becoming powerful assistants that shorten R&D cycles and reduce costly trial-and-error experimentation.

Automation exposure: AI can automate literature reviews, property prediction, simulation setup, microstructure image analysis, defect detection in quality control, and high-throughput screening of candidate materials. Routine data analysis and report generation are also increasingly automated.

The human edge: Materials engineers bring hands-on experimental judgment, physical intuition about why materials fail, creative problem-solving across competing constraints (cost, performance, sustainability, manufacturability), and the ability to validate AI predictions in the lab. Cross-functional collaboration, regulatory navigation, and accountability for safety-critical decisions remain distinctly human.

Figures are estimates for exploration — verify current data with BLS.gov.