Will AI replace Materials Engineers?

Not as a job, but it is already doing parts of the work. Across the 21 official task statements scored for Materials Engineers (United States, SOC 17-2131), 34% of the importance-weighted core work is made of tasks today's AI could already do most of. The overall exposure score is 44 out of 100 (range 38–50, band: partial). That is a statement about tasks, not about headcount: this measures what AI could do, not whether any employer adopts it, whether the law allows it, or whether doing the routine parts faster creates more demand for the human parts. Figures are from release 2026-q4.1.

Which tasks in “Materials Engineers” can AI already do?

The highest-scoring tasks in release 2026-q4.1 are: “Review new product plans, and make recommendations for material selection, based on design objectives such as strength, weight, heat resistance, electrical c…” (83/100, very high); “Write for technical magazines, journals, and trade association publications” (83/100, very high); “Analyze product failure data and laboratory test results to determine causes of problems and develop solutions” (75/100, high). Each score comes from five published 0–4 ratings turned into a number by a published formula, and each carries the model's one-sentence reason on the page.

Which tasks in “Materials Engineers” stay human?

About 61% of this job's task weight sits in work that scores low for AI exposure. The lowest-scoring tasks in release 2026-q4.1 are: “Supervise production and testing processes in industrial settings, such as metal refining facilities, smelting or foundry operations, or nonmetallic material…” (7/100, minimal); “Modify properties of metal alloys, using thermal and mechanical treatments” (8/100, minimal); “Conduct or supervise tests on raw materials or finished products to ensure their quality” (17/100, minimal). Low scores usually mean the task needs a body in a room, a legally accountable human, or trust built in real time. Those are the three things the scoring rubric treats as gates rather than obstacles.

What should someone working in “Materials Engineers” do about AI?

Start from the ledger rather than the headline: 34% of this job's weighted core work is exposed, and roughly 61% is not. The practical move is to spend more of your week on the tasks that score low, the ones above, and to get fluent at directing AI through the tasks that score high, because those are the parts that change whether or not you are ready for them. This page does not predict your job, and nothing here is career advice tailored to you: the score describes the occupation, not the person.

How is the AI exposure score for Materials Engineers calculated?

Each official task statement for the occupation is rated on five published 0–4 dimensions (output replicability, physical embodiment, licensed accountability, real-time human trust, and data availability) by claude-opus-5 using scoring prompt task_scoring_v1.0. The model never writes the score; a published formula turns the five ratings into a 0–100 number, so every score can be recomputed by hand. The occupation figure is the importance-weighted mean across 21 scored tasks. The prompt, the rubric, the formula and the full dataset are published at https://futureproof.collab365.com/method and https://futureproof.collab365.com/data/2026-q4.1 under CC BY 4.0.

US dataswitch to UK

Materials Engineers

analyzing product failure data and laboratory test results to determine causes of problems and develop solutions, guiding technical staff in developing materials for specific uses in projected products or devices and reviewing new product plans. If that's your week, this page is about your job.

The honest answer

AI is already taking a real slice of the routine work here: analyzing product failure data and laboratory test results to determine causes of problems and develop solutions. That is a slice of tasks, not of you.

Your move: what you can actually do about this ↓

Where the work sits, by task weight

shifting to AI: 34%
changing shape: 5%
staying human: 61%

These bars are tasks changing hands, not people being counted out. The ledger below shows which.

Whole-job exposure score 44 out of 100 (38–50 allowing for uncertainty): partial exposure, across 21 scored tasks. The number is the support for the sentence above it, not a headline about anyone’s future.

How we know this

What is measured: Every published task statement for materials engineers is rated on five dimensions: can a model produce the output, does the work need a body in a room, does it need a legally accountable person, does it depend on a person being trusted in the moment, and how much data exists. A published formula turns those five ratings into the score; the model never writes the number.

How the bar is built: Each task’s share of the bar is its published importance weight, so a task you do all day counts for more than one you do twice a year.

Release: 2026-q4.1, scores computed 2026-08-05. Read the full method.

That slice is not coming back; the core of the job, supervising the work of technologists, stays yours. The tools change hands, the accountability doesn't.

Your week, as this page understands it

Evaluate materials and develop machinery and processes to manufacture materials for use in products that must meet specialized design and performance specifications. Develop new uses for known materials. Includes those engineers working with composite materials or specializing in one type of material, such as graphite, metal and metal alloys, ceramics and glass, plastics and polymers, and naturally occurring materials. Includes metallurgists and metallurgical engineers, ceramic engineers, and welding engineers. The job title says “materials engineers”. The real job is the part underneath: supervising the work of technologists. That is the thing someone has to be right about.

The exposed part of this job is specific, and we won’t pretend it is coming back. But materials engineers is not one task. It is 21 scored ones, and they are nowhere near equally exposed. The most durable of them, on this evidence, is supervising the work of technologists, and the ledger below shows exactly why.

Where the work sits, by task weight

shifting to AI: 34%
changing shape: 5%
staying human: 61%

These bars are tasks changing hands, not people being counted out. The ledger below shows which.

How we know this

How the bar is built: Each task’s share of the bar is its published importance weight, so a task you do all day counts for more than one you do twice a year.

Release: 2026-q4.1, scores computed 2026-08-05. Read the full method.

Your job, task by task

These are the official task statements for this occupation, in plain English, sorted by what the evidence says is happening to each one. The official wording sits under every line so you can check the rewrite against it.

4 of this occupation's scored task statements carry a score that was measured under a different occupation's context, because the statement is shared between jobs and has only been scored once so far. Each one names the occupation it was measured under in the free routes below; none is presented as a measurement for this job.
Task clusters are not derived in this release, so the task-cluster field is empty and no Collab365 Space routing is attached to this occupation yet.

Shifting to AI

7 tasks

Tasks today’s tools can already do most of. This is the part we will not soften: where these rows are the bulk of your week, the week changes.

Analyzing product failure data and laboratory test results to determine causes of problems and develop solutions
This is reading one thing and writing another: product failure data in, a record out. That is the shape today's tools are built for.
importance 4 · Core
Source: “Analyze product failure data and laboratory test results to determine causes of problems and develop solutions.” (O*NET task statement)
How this row was scored
Exposure score: 75 out of 100 (71–79 allowing for uncertainty): high exposure, high confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Working through failure and test data to find causes is analysis software does well on documented methods.
The five ratings: output a model can produce 3/4 · needs a body in a room 0/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Evaluating technical specifications and economic factors relating to process or product design objectives
This is reading one thing and writing another: technical specifications in, a record out. That is the shape today's tools are built for.
importance 4 · Core
Source: “Evaluate technical specifications and economic factors relating to process or product design objectives.” (O*NET task statement)
How this row was scored
Exposure score: 75 out of 100 (71–79 allowing for uncertainty): high exposure, high confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Weighing specifications against cost is structured desk analysis software handles well.
The five ratings: output a model can produce 3/4 · needs a body in a room 0/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Determining appropriate methods for fabricating and joining materials
This is reading one thing and writing another: appropriate methods in, a record out. That is the shape today's tools are built for.
importance 4 · Core
Source: “Determine appropriate methods for fabricating and joining materials.” (O*NET task statement)
How this row was scored
Exposure score: 75 out of 100 (71–79 allowing for uncertainty): high exposure, high confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Choosing fabrication and joining methods draws on widely published materials engineering references.
The five ratings: output a model can produce 3/4 · needs a body in a room 0/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Reviewing new product plans and making recommendations for material selection
This is reading one thing and writing another: new product plans in, a record out. That is the shape today's tools are built for.
importance 4 · Core
Source: “Review new product plans, and make recommendations for material selection, based on design objectives such as strength, weight, heat resistance, electrical conductivity, and cost.” (O*NET task statement)
How this row was scored
Exposure score: 83 out of 100 (79–87 allowing for uncertainty): very high exposure, high confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Matching materials to strength, weight, heat and cost targets uses published property data software searches well.
The five ratings: output a model can produce 3/4 · needs a body in a room 0/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 4/4.
Performing managerial functions, such as preparing proposals and budgets, analyzing labor costs and writing reports
This is reading one thing and writing another: managerial functions in, a record out. That is the shape today's tools are built for.
importance 3 · Core
Source: “Perform managerial functions, such as preparing proposals and budgets, analyzing labor costs, and writing reports.” (O*NET task statement)
How this row was scored
Exposure score: 75 out of 100 (71–79 allowing for uncertainty): high exposure, high confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Proposals, budgets and reports are structured documents software drafts well from supplied figures.
The five ratings: output a model can produce 3/4 · needs a body in a room 0/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.

Changing shape

1 task

Tasks where the machine takes the producing and a person keeps the checking, the deciding, or the answering-for-it. For most jobs this is the biggest group, and it is where "transformation, not termination" is literally visible.

Planning and evaluating new projects
The software now makes the first pass at new projects, but the part that matters is a person saying it and standing behind it. So the job becomes checking and deciding rather than producing.
importance 4 · Core
Source: “Plan and evaluate new projects, consulting with other engineers and corporate executives, as necessary.” (O*NET task statement)
How this row was scored
Exposure score: 40 out of 100 (33–47 allowing for uncertainty): partial exposure, medium confidence.
Why it sits in this group: the same decision, made over and over; the value is that a specific person does it.
The rating behind it: Project evaluation is documented analysis, though the decisions come out of discussions with colleagues.
The five ratings: output a model can produce 2/4 · needs a body in a room 0/4 · needs an accountable person 0/4 · needs to be trusted in the moment 2/4 · how much data exists 3/4.

Staying human

13 tasks

Tasks that stay with a person, because they happen in the physical world, because the rules need someone accountable, or because the value is that a specific person does them.

Guiding technical staff in developing materials for specific uses in projected products or devices
The value here is that a specific person handles technical staff and stands behind it. That is earned, not computed.
importance 4 · Core
Source: “Guide technical staff in developing materials for specific uses in projected products or devices.” (O*NET task statement)
How this row was scored
Exposure score: 30 out of 100 (23–37 allowing for uncertainty): low exposure, medium confidence.
Why it sits in this group: the same decision, made over and over; the value is that a specific person does it.
The rating behind it: Technical direction can be drafted, but guiding staff depends on working closely with them.
The five ratings: output a model can produce 2/4 · needs a body in a room 1/4 · needs an accountable person 0/4 · needs to be trusted in the moment 2/4 · how much data exists 3/4.
Designing and directing the testing or control of processing procedures
The ratings behind this row put the testing or control of processing procedures well outside what today's tools can do on their own.
importance 4 · Core
Source: “Design and direct the testing or control of processing procedures.” (O*NET task statement)
How this row was scored
Exposure score: 37 out of 100 (30–44 allowing for uncertainty): low exposure, medium confidence.
Why it sits in this group: the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Test plans can be drafted from standards, but directing the work involves people and equipment on site.
The five ratings: output a model can produce 2/4 · needs a body in a room 1/4 · needs an accountable person 0/4 · needs to be trusted in the moment 1/4 · how much data exists 3/4.
Monitoring material performance and evaluating its deterioration
This work happens in the physical world: material performance, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Monitor material performance, and evaluate its deterioration.” (O*NET task statement)
How this row was scored
Exposure score: 29 out of 100 (22–36 allowing for uncertainty): low exposure, medium confidence.
Why it sits in this group: the same decision, made over and over; mistakes that are cheap to catch; work that happens in the physical world.
The rating behind it: Deterioration models are documented, though monitoring real material usually means inspecting or sampling it.
The five ratings: output a model can produce 2/4 · needs a body in a room 2/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Supervising the work of technologists
The value here is that a specific person handles the work of technologists and stands behind it. That is earned, not computed.
importance 4 · Core
Source: “Supervise the work of technologists, technicians, and other engineers and scientists.” (O*NET task statement)
How this row was scored
Exposure score: 17 out of 100 (10–24 allowing for uncertainty): minimal exposure, medium confidence.
Why it sits in this group: the value is that a specific person does it.
The rating behind it: Supervising scientists and technicians depends on knowing the people and the work in front of them.
The five ratings: output a model can produce 1/4 · needs a body in a room 1/4 · needs an accountable person 0/4 · needs to be trusted in the moment 2/4 · how much data exists 2/4.

Show the other 11 tasks

Writing for technical magazines, journals and trade association publications
shifting to AI
This is reading one thing and writing another: technical magazines, journals and trade association publications in, a record out. That is the shape today's tools are built for.
importance 3 · Core
Source: “Write for technical magazines, journals, and trade association publications.” (O*NET task statement)
How this row was scored
Exposure score: 83 out of 100 (79–87 allowing for uncertainty): very high exposure, high confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Writing articles for technical publications is squarely what these tools do well.
The five ratings: output a model can produce 3/4 · needs a body in a room 0/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 4/4.
Replicating the characteristics of materials and their components
shifting to AI
This is reading one thing and writing another: the characteristics of materials in, a record out. That is the shape today's tools are built for.
importance 3 · Core
Source: “Replicate the characteristics of materials and their components, using computers.” (O*NET task statement)
How this row was scored
Exposure score: 75 out of 100 (71–79 allowing for uncertainty): high exposure, high confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Simulating material behaviour on a computer is exactly what modelling software is for.
The five ratings: output a model can produce 3/4 · needs a body in a room 0/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Conducting training sessions on new material products
staying human
The value here is that a specific person handles sessions and stands behind it. That is earned, not computed.
importance 4 · Core
Source: “Conduct training sessions on new material products, applications, or manufacturing methods for customers and their employees.” (O*NET task statement)
How this row was scored
Exposure score: 39 out of 100 (32–46 allowing for uncertainty): low exposure, medium confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; the value is that a specific person does it.
The rating behind it: Training material is easy to produce, though customers expect a knowledgeable person to deliver it.
The five ratings: output a model can produce 3/4 · needs a body in a room 1/4 · needs an accountable person 0/4 · needs to be trusted in the moment 2/4 · how much data exists 3/4.
Solving problems in a number of engineering fields
staying human
The ratings behind this row put problems well outside what today's tools can do on their own.
importance 4 · Core
Source: “Solve problems in a number of engineering fields, such as mechanical, chemical, electrical, civil, nuclear, and aerospace.” (O*NET task statement)
How this row was scored
Exposure score: 38 out of 100 (31–45 allowing for uncertainty): low exposure, medium confidence.
Why it sits in this group: the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Cross-discipline problem solving is well supported by tools, but the answers need engineering judgement.
The five ratings: output a model can produce 2/4 · needs a body in a room 1/4 · needs an accountable person 1/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Presenting technical information at conferences
staying human
This work happens in the physical world: technical information, in a real place. Software cannot follow it there.
importance 3 · Core
Source: “Present technical information at conferences.” (O*NET task statement)
How this row was scored
Exposure score: 35 out of 100 (28–42 allowing for uncertainty): low exposure, medium confidence.
Why it sits in this group: the same decision, made over and over; mistakes that are cheap to catch; work that happens in the physical world.
The rating behind it: Slides and papers are easy to prepare, but presenting at a conference means standing up in front of people.
The five ratings: output a model can produce 3/4 · needs a body in a room 2/4 · needs an accountable person 0/4 · needs to be trusted in the moment 1/4 · how much data exists 4/4.
Planning and implementing laboratory operations to develop material and fabrication procedures that meet cost
staying human
This work happens in the physical world: laboratory operations, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Plan and implement laboratory operations to develop material and fabrication procedures that meet cost, product specification, and performance standards.” (O*NET task statement)
How this row was scored
Exposure score: 29 out of 100 (22–36 allowing for uncertainty): low exposure, medium confidence.
Why it sits in this group: the same decision, made over and over; mistakes that are cheap to catch; work that happens in the physical world.
The rating behind it: Plans can be drafted, but running laboratory operations and fabrication trials is hands-on.
The five ratings: output a model can produce 2/4 · needs a body in a room 2/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Designing processing plants and equipment
staying human
The rules require a named, qualified person to answer for plants, and that person cannot be a piece of software.
importance 3 · Core
Source: “Design processing plants and equipment.” (O*NET task statement)
How this row was scored
Exposure score: 23 out of 100 (16–30 allowing for uncertainty): low exposure, medium confidence.
Why it sits in this group: the same decision, made over and over; someone qualified has to answer for it.
The rating behind it: Plant and equipment design carries formal engineering responsibility and needs original design judgment.
The five ratings: output a model can produce 1/4 · needs a body in a room 1/4 · needs an accountable person 2/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Teaching in colleges and universities
staying human
This work happens in the physical world: colleges, in a real place. Software cannot follow it there.
importance 3 · Supplemental
Source: “Teach in colleges and universities.” (O*NET task statement)
How this row was scored
Exposure score: 20 out of 100 (13–27 allowing for uncertainty): low exposure, medium confidence.
Why it sits in this group: the same decision, made over and over; work that happens in the physical world; the value is that a specific person does it.
The rating behind it: Teaching a university class means standing in front of students and reading the room.
The five ratings: output a model can produce 2/4 · needs a body in a room 2/4 · needs an accountable person 0/4 · needs to be trusted in the moment 2/4 · how much data exists 3/4.
Conducting or supervising tests on raw materials or finished products to ensure their quality
staying human
This work happens in the physical world: tests, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Conduct or supervise tests on raw materials or finished products to ensure their quality.” (O*NET task statement)
How this row was scored
Exposure score: 17 out of 100 (10–24 allowing for uncertainty): minimal exposure, medium confidence.
Why it sits in this group: the same decision, made over and over; work that happens in the physical world.
The rating behind it: Quality testing runs on physical samples and instruments, with a person overseeing the work.
The five ratings: output a model can produce 1/4 · needs a body in a room 2/4 · needs an accountable person 0/4 · needs to be trusted in the moment 1/4 · how much data exists 3/4.
Modifying properties of metal alloys
staying human
This work happens in the physical world: properties of metal alloys, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Modify properties of metal alloys, using thermal and mechanical treatments.” (O*NET task statement)
How this row was scored
Exposure score: 8 out of 100 (4–12 allowing for uncertainty): minimal exposure, high confidence.
Why it sits in this group: work that happens in the physical world.
The rating behind it: Heat treating and working metal happens in a furnace or press with someone operating it.
The five ratings: output a model can produce 1/4 · needs a body in a room 3/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 2/4.
Supervising production and testing processes in industrial settings
staying human
This work happens in the physical world: production, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Supervise production and testing processes in industrial settings, such as metal refining facilities, smelting or foundry operations, or nonmetallic materials production operations.” (O*NET task statement)
How this row was scored
Exposure score: 7 out of 100 (3–11 allowing for uncertainty): minimal exposure, high confidence.
Why it sits in this group: work that happens in the physical world.
The rating behind it: Overseeing a foundry or refining line means being present where the process runs.
The five ratings: output a model can produce 1/4 · needs a body in a room 3/4 · needs an accountable person 0/4 · needs to be trusted in the moment 1/4 · how much data exists 2/4.

What this job pays, and how many people do it

Median pay: $112,860a year, the middle of the range, so half earn more and half earn less.bls-oews, 2025 · May 2025 estimates (national_M2025_dl.xlsx)
How we know this
Source: bls-oews
Reference period: May 2025 estimates (national_M2025_dl.xlsx)
Rounding: Shown as published.
People doing this job: 22,770in the US, 2025.bls-oews · May 2025 estimates (national_M2025_dl.xlsx)

What is deliberately not here: a forecast of how many of these jobs exist in ten years. Where an official projection exists for a market we publish it with its vintage; where it does not, we leave the space empty rather than borrow the other country’s number.

Why this is shifting

The reason is boringly specific. Most of what is shifting here is reading one thing and writing another: product failure data in, a record out. The rows above are exactly that shape: analyzing product failure data and laboratory test results to determine causes of problems and develop solutions and evaluating technical specifications and economic factors relating to process or product design objectives. What it cannot do is be trusted in person, which is what the work of technologists runs on: someone specific doing it and standing behind it. Which is why this page talks about your tasks changing, not your job ending.

Your move

Over a pint: what I’d tell you if you were my friend

Your week is splitting in two, and which half fills it is the whole question. Analyzing product failure data and laboratory test results to determine causes of problems and develop solutions is going; supervising the work of technologists is not.

So, given all that: 34% of this job's task weight sits in rows the software is already learning, 5% in rows that change shape rather than disappear, and 61% in rows it is nowhere near. That is the position, measured across 21 scored tasks. It is not a forecast about you.

The occupation is an average and you are not, so the first job below is finding out where your own week actually sits.

This week: one thing

Print the task ledger above and put your own hours next to each row. Roughly, in pen, in one sitting. Then look at which group your hours actually pile up in. Twenty minutes, this week.

What you end up holding: your own week, on one page, sorted into what is shifting and what is not
How long it takes: about twenty minutes

If printing it isn’t practical: Read the rows off this page and write the same list on the back of an envelope. Same twenty minutes, same page, and it works just as well said out loud to someone who knows the job. The point is your hours next to the rows, not the paper it is on.

Over the next 90 days

Volunteer toward the durable end, visibly. Over the next ninety days put your hand up for the work in the bottom group (supervising the work of technologists) and let people see you doing it. Not a new project: the same job, with your mix deliberately tilted. The point is that when the rota or the reorganisation comes, the version of you people picture is the one doing the part that stays.

Over the next 12 months

Claim a specialism at the durable end and let the other end go. Over a year, deliberately become the person who handles supervising the work of technologists, and deliberately stop being the first choice for the rows in the top group. That trade costs something, because the exposed work is often the comfortable work. Decide it on purpose rather than by drift. Before you pay for anything, use CareerOneStop - Find local training. It is free, it is the Labor Department's own service, and it is listed below with the rest of the free routes.

The roads out of here, and why I am not sending you down them

I looked at the obvious moves out of this job, and here is what I found.

I checked the 12 nearest US occupations to materials engineers (nearest by the work that AI is not taking, not by job title), and none of them survived. The closest was materials scientists: only about 8% of its durable work is work you already do. Your own job splits about 34/66: that share of the list sits in the top exposure band and the rest does not. On this evidence the honest move is inside the job rather than out of it. Become the person who owns “guide technical staff in developing materials for specific uses in projected products…”, and let the exposed end go.

How that was checked: this job was compared against all 830 US occupations in this release on their official task statements, and the 12 nearest were examined one by one. A move that turns on an industry, an employer or a qualification rather than on the work itself will not show up in a check like that. And this release carries no licence register, so anything you are weighing needs that looked up separately.

3 moves I checked and rejected

These are the obvious-looking jumps. They are here with their reasons rather than quietly dropped, because the ones that fail are worth knowing about. It is one less thing to turn over at night.

Materials Scientists
Why it looked obvious: It came up as a near neighbour because one of your tasks is on their list in the same words: “teach in colleges and universities”. Across the whole of both lists that adds up to about 8% of the work in that job the software is not taking.
Why I am not recommending it: Almost none of it is work you already do: about 8% of the durable side of that job. That is a different job, not a next step.
Look at that job’s page anyway →
Helpers--Production Workers
Why it looked obvious: It came up as a near neighbour on the work AI is not taking: you already conduct or supervise tests on raw materials or finished products to ensure their…, and their equivalent is to lift raw materials, finished products, and packed items, manually or using hoists. Across both published task lists that is about 3% of the durable work in that job.
Why I am not recommending it: Almost none of it is work you already do: about 3% of the durable side of that job. That is a different job, not a next step. It is a pay cut, in those words: $39,070 against your $112,860, 65.4% less (OEWS May 2025 (both)). Retraining to earn less is a decision, not advice.
Look at that job’s page anyway →
Industrial Engineers
Why it looked obvious: It came up as a near neighbour on the work AI is not taking: you already conduct or supervise tests on raw materials or finished products to ensure their…, and their equivalent is to draw samples of raw materials, intermediate products, or finished products for validation testing. Across both published task lists that is about 2% of the durable work in that job.
Why I am not recommending it: Almost none of it is work you already do: about 2% of the durable side of that job. That is a different job, not a next step.
Look at that job’s page anyway →

What I’d stop worrying about

A friend tells you what not to spend fear on. This is that list.

The headline number you read somewhere
The big “X% of jobs” figures are about the whole economy, not about you. The number that describes your job is on this page: 34% of its task weight, across 21 scored tasks. Every row behind it is printed above with the source. Check ours; ignore theirs.
The whole-job doom story
Nothing on this page says this job ends. It says the mix inside it moves. Half the rows above are unchanged or changing shape, and the plan is about which half your week sits in. That is a very different problem, and a solvable one.
Panic-buying a course
Do the twenty-minute sorting exercise first. Most people who buy a course before they have done it buy the wrong one, and the free services listed below will tell you the same thing without charging for it.
The “obvious” next job everyone suggests
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You are reading the United States figures

The United Kingdom splits this work across more than one official group, of which Engineering professionals n.e.c. is the closest. The pay and employment figures are not directly comparable, and we do not average them together.

Switch to the United Kingdom page →partial match

The other groups this work is counted across:

In UK official statistics this job is counted as Engineering professionals n.e.c., Quantity surveyors, Quality control and planning engineers and Engineering project managers and project engineers. Pay is shown separately for each of those groups (medians cannot be averaged together), while the task list and the scores on this page are for this group only.

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No Space for this job, but one for what is happening to it

Nothing Collab365 runs is built for materials engineers, and we are not going to point you at the nearest one and call it a fit.

There is one that is not about a job title at all. The AI Authority is about being the person who directs these tools at work rather than the person they get compared to. That is worth saying here, because 34% of the work on this page is already inside what they can do.

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The AI Authority is a general community about working with AI, not a course for materials engineers. You do not need it to act on anything here: the moves above cost nothing and stand on their own. The data on this page is the same either way.

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No Space for materials engineers yet. Should there be one?

Collab365 Spaces is built by the same people as this site. We find the problems that AI and automation are creating inside one kind of work, then solve them as short courses, briefings and Blueprints. Each Space is the community too, so the research and the people doing your job are in the same place.

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Collab365 launches new communities where the need is real. If one for materials engineers existed, with researched problems, courses and people in the same boat, would you want in?

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No deadline on any of this. The page will still be here, and the data is refreshed on a published schedule rather than when someone wants a headline.

Questions people ask about this job

Will AI replace Materials Engineers?: Not as a job, but it is already doing parts of the work. Across the 21 official task statements scored for Materials Engineers (United States, SOC 17-2131), 34% of the importance-weighted core work is made of tasks today's AI could already do most of. The overall exposure score is 44 out of 100 (range 38–50, band: partial). That is a statement about tasks, not about headcount: this measures what AI could do, not whether any employer adopts it, whether the law allows it, or whether doing the routine parts faster creates more demand for the human parts. Figures are from release 2026-q4.1.
Which tasks in “Materials Engineers” can AI already do?: The highest-scoring tasks in release 2026-q4.1 are: “Review new product plans, and make recommendations for material selection, based on design objectives such as strength, weight, heat resistance, electrical c…” (83/100, very high); “Write for technical magazines, journals, and trade association publications” (83/100, very high); “Analyze product failure data and laboratory test results to determine causes of problems and develop solutions” (75/100, high). Each score comes from five published 0–4 ratings turned into a number by a published formula, and each carries the model's one-sentence reason on the page.
Which tasks in “Materials Engineers” stay human?: About 61% of this job's task weight sits in work that scores low for AI exposure. The lowest-scoring tasks in release 2026-q4.1 are: “Supervise production and testing processes in industrial settings, such as metal refining facilities, smelting or foundry operations, or nonmetallic material…” (7/100, minimal); “Modify properties of metal alloys, using thermal and mechanical treatments” (8/100, minimal); “Conduct or supervise tests on raw materials or finished products to ensure their quality” (17/100, minimal). Low scores usually mean the task needs a body in a room, a legally accountable human, or trust built in real time. Those are the three things the scoring rubric treats as gates rather than obstacles.
What should someone working in “Materials Engineers” do about AI?: Start from the ledger rather than the headline: 34% of this job's weighted core work is exposed, and roughly 61% is not. The practical move is to spend more of your week on the tasks that score low, the ones above, and to get fluent at directing AI through the tasks that score high, because those are the parts that change whether or not you are ready for them. This page does not predict your job, and nothing here is career advice tailored to you: the score describes the occupation, not the person.
How is the AI exposure score for Materials Engineers calculated?: Each official task statement for the occupation is rated on five published 0–4 dimensions (output replicability, physical embodiment, licensed accountability, real-time human trust, and data availability) by claude-opus-5 using scoring prompt task_scoring_v1.0. The model never writes the score; a published formula turns the five ratings into a 0–100 number, so every score can be recomputed by hand. The occupation figure is the importance-weighted mean across 21 scored tasks. The prompt, the rubric, the formula and the full dataset are published at https://futureproof.collab365.com/method and https://futureproof.collab365.com/data/2026-q4.1 under CC BY 4.0.

Where these numbers come from

Worth knowing about these figures

The match between this job and its counterpart in the other country is partial, so the two pages count slightly different groups of people.
4 of this occupation's scored task statements carry a score that was measured under a different occupation's context, because the statement is shared between jobs and has only been scored once so far. Each one names the occupation it was measured under in the free routes below; none is presented as a measurement for this job.
Task clusters are not derived in this release, so the task-cluster field is empty and no Collab365 Space routing is attached to this occupation yet.

Task statements: onet-dbProcessing: catalogue-bridge → onet-im-rt-weighting → task-scoring → score-aggregation
Task weights: onet-db (im-rt)
Scores: Rubric task_scoring_v1.0, model claude-opus-5, computed 2026-08-05.
Pay and employment: bls-oews (May 2025 estimates (national_M2025_dl.xlsx))bls-oews (May 2025 estimates (national_M2025_dl.xlsx))

Figures on this page come from release 2026-q4.1, published 2026-08-05. Every release keeps its own permanent address, so a figure you cite in March is still there, unchanged, in November.

The plain-English wording on this page is assembled directly from the task statements and the published ratings, not written by hand for this occupation. That is why it is specific, and it is also why we say so.

The routes and free resources further up are today’s, not the release’s (last reviewed 2026-08-05). A route is an offer, not a historical fact, so it moves on its own clock.

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Everything on this site is published under CC BY 4.0. Quote it, chart it, sell something built on it. Just say where it came from, and cite the dated release rather than the site, so the figure you quote stays checkable.

Plain text

Collab365 (2026). Collab365 Futureproof: task-level AI exposure for US and UK occupations, release 2026-q4.1 (methodVersion 2.0.0, promptVersion task_scoring_v1.0). https://futureproof.collab365.com/data/2026-q4.1. Licensed CC BY 4.0. Built with O*NET data (USDOL/ETA, CC BY 4.0); ONS data (Open Government Licence v3.0); GAISI task framework (arXiv:2507.22748, MIT); BLS data (public domain).

BibTeX

@misc{collab365futureproof2026q41,
  title        = {Collab365 Futureproof: task-level AI exposure for US and UK occupations, release 2026-q4.1},
  author       = {{Collab365}},
  year         = {2026},
  url          = {https://futureproof.collab365.com/data/2026-q4.1},
  note         = {Release 2026-q4.1, methodVersion 2.0.0, promptVersion task_scoring_v1.0, CC BY 4.0}
}

Permalink

https://futureproof.collab365.com/data/2026-q4.1

Data as of release 2026-q4.1, published 2026-08-05. Releases never change after publication; when the figures move, a new dated release is published beside this one and this one stays exactly where it is.