Materials engineers

Prepare proposals, budgets, and reports analyzing labor costs

Preparing technical proposals, budgets, and cost analysis reports from project data is highly structured document generation that AI handles well. Current AI can draft proposals from templates, calculate labor costs, format budgets, and generate reports with minimal human input beyond initial parameters and final review.

BLS evidence: Prepare proposals and budgets, analyze labor costs, and write reports.

Assess economic, environmental, and sustainability factors related to materials

AI can analyze lifecycle data, calculate environmental impacts, compare costs across suppliers, and assess sustainability metrics using established frameworks and databases. This analytical task with well-defined inputs and outputs is highly suitable for AI, requiring human oversight mainly for strategic trade-off decisions.

BLS evidence: Assess economic, environmental, and sustainability factors related to producing, using, and disposing of materials.

Evaluate technical and quality control specifications of materials

Evaluating specifications against standards, test data, and quality metrics is highly structured work that AI can perform effectively. AI can cross-reference specifications with databases, identify non-conformances, and flag quality issues, with human review primarily for edge cases and final approval.

BLS evidence: Evaluate technical and quality control specifications of materials.

Select appropriate materials for specific products and processes

Material selection involves matching properties to requirements using databases, standards, and performance criteria—tasks AI handles well. Modern AI can process specifications, compare alternatives, and recommend materials based on multi-criteria optimization, though engineers typically review selections for non-obvious constraints.

BLS evidence: They also help select materials for specific products and identify ways to use existing materials.

Monitor performance and degradation of materials over time

AI excels at analyzing time-series degradation data, predicting failure curves, and identifying anomalies in sensor data from materials under service. However, setting up monitoring systems and interpreting results in context of specific applications still requires significant human involvement, though AI handles much of the analytical workload.

BLS evidence: Monitor the performance and degradation of materials over time.

Study properties and structures of metals, polymers, and other substances

AI can analyze existing literature, predict material properties from structure using ML models, and process characterization data, but physical specimen preparation and operation of analytical instruments (SEM, XRD, tensile testers) require human execution. AI substantially accelerates analysis but humans remain load-bearing for experimental work.

BLS evidence: They study the properties and structures of metals, polymers, and other substances to develop new materials.

Determine causes of material failure and develop corrective solutions

Failure analysis requires physical examination of failed components, microscopy, chemical analysis, and contextual understanding of operating conditions. AI can assist with pattern recognition in failure modes and suggest hypotheses, but root cause determination typically requires hands-on investigation and expert judgment.

BLS evidence: Determine causes of material failure and develop ways of overcoming such failure.

Plan and evaluate new projects in consultation with other engineers and managers

Project planning and cross-functional consultation require understanding organizational dynamics, negotiating priorities, reading interpersonal cues, and making judgment calls about feasibility and risk that depend heavily on tacit knowledge and relationship management. AI can support with data analysis and scheduling but humans drive the collaborative process.

BLS evidence: Plan and evaluate new projects, consulting with other engineers and managers as necessary.

Design and implement procedures to develop, process, test, and deploy materials

Procedure design involves physical process constraints, equipment limitations, and safety considerations that require hands-on knowledge and iterative refinement in laboratory/manufacturing settings. AI can draft procedures and suggest parameters, but validation and implementation require human judgment and physical presence.

BLS evidence: Design and implement procedures to develop, process, test, and deploy materials.

Develop and test new materials for specific product applications

Developing new materials requires extensive physical experimentation, hands-on laboratory work with specialized equipment, and iterative testing in real-world conditions that AI cannot directly perform. AI can assist with simulation and property prediction, but the core experimental work remains human-driven.

BLS evidence: Materials engineers develop, process, and test materials used to create a range of products.

Supervise technologists, technicians, and other engineers and scientists

Supervising technical staff requires real-time presence, mentoring, performance management, conflict resolution, and adaptive leadership based on individual team member needs and dynamics. These deeply interpersonal and contextual tasks are far beyond current AI capabilities in workplace settings.

BLS evidence: Supervise the work of technologists, technicians, and other engineers and scientists.