US dataswitch to UK

Heat Treating Equipment Setters, Operators, and Tenders, Metal and Plastic

reading production schedules and work orders to determine processing sequences, adjusting controls to maintain temperatures and heating times, removing parts from furnaces after specified times and signaling forklift operators to deposit or extract containers of parts into and from furnaces and quenching rinse tanks. If that's your week, this page is about your job.

The honest answer

AI changes the edges of this job, not the middle: setting up and operating or tend machines is work software can't reach.

Your move: what you can actually do about this ↓

Where the work sits, by task weight

shifting to AI: 7%
changing shape: 14%
staying human: 79%

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

Whole-job exposure score 14 out of 100 (12–19 allowing for uncertainty): minimal exposure, across 23 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 heat treating equipment setters, operators, and tenders, metal and plastic 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.

What shifts is reading production schedules and work orders to determine processing sequences. This page scores what today's tools actually do, not headlines.

Your week, as this page understands it

Set up, operate, or tend heating equipment, such as heat-treating furnaces, flame-hardening machines, induction machines, soaking pits, or vacuum equipment to temper, harden, anneal, or heat treat metal or plastic objects. The job title says “heat treating equipment setters”, “operators”, “tenders”, “metal” or “plastic”: officially one job, several names. The real job is the part underneath: setting up and operating or tend machines. 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 heat treating equipment setters, operators, and tenders, metal and plastic is not one task. It is 23 scored ones, and they are nowhere near equally exposed. The most durable of them, on this evidence, is setting up and operating or tend machines, and the ledger below shows exactly why.

Where the work sits, by task weight

shifting to AI: 7%
changing shape: 14%
staying human: 79%

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.

1 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

1 task

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.

Reading production schedules and work orders to determine processing sequences
This is reading one thing and writing another: production schedules in, a record out. That is the shape today's tools are built for.
importance 5 · Core
Source: “Read production schedules and work orders to determine processing sequences, furnace temperatures, and heat cycle requirements for objects to be heat-treated.” (O*NET task statement)
How this row was scored
Exposure score: 69 out of 100 (62–76 allowing for uncertainty): high exposure, medium 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: Pulling sequences, temperatures and cycle times out of work orders is straightforward document work.
The five ratings: output a model can produce 4/4 · needs a body in a room 1/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.

Changing shape

3 tasks

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.

Recording times that parts are removed from furnaces to document that objects have attained specified temperatures for specified times
The software now makes the first pass at times, but part of it still happens in the physical world. So the job becomes checking and deciding rather than producing.
importance 5 · Core
Source: “Record times that parts are removed from furnaces to document that objects have attained specified temperatures for specified times.” (O*NET task statement)
How this row was scored
Exposure score: 46 out of 100 (42–50 allowing for uncertainty): partial exposure, high 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: Recording when parts came out of the furnace is simple logging that equipment can timestamp itself.
The five ratings: output a model can produce 4/4 · 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.
Determining types and temperatures of baths and quenching media needed to attain specified part hardness
The software now makes the first pass at types, but the deciding part still needs a person. So the job becomes checking and deciding rather than producing.
importance 5 · Supplemental
Source: “Determine types and temperatures of baths and quenching media needed to attain specified part hardness, toughness, and ductility, using heat-treating charts and knowledge of methods, equipment, and metals.” (O*NET task statement)
How this row was scored
Exposure score: 56 out of 100 (49–63 allowing for uncertainty): partial exposure, medium 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 bath types and quench media comes from published heat-treating charts and standard practice.
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 0/4 · how much data exists 3/4.
Determining flame temperatures, current frequencies, heating cycles and induction heating coils
The software now makes the first pass at flame temperatures, current frequencies, but the deciding part still needs a person. So the job becomes checking and deciding rather than producing.
importance 5 · Supplemental
Source: “Determine flame temperatures, current frequencies, heating cycles, and induction heating coils needed, based on degree of hardness required and properties of stock to be treated.” (O*NET task statement)
How this row was scored
Exposure score: 56 out of 100 (49–63 allowing for uncertainty): partial exposure, medium 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 out temperatures, frequencies and cycles for a required hardness follows documented metallurgy.
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 0/4 · how much data exists 3/4.

Staying human

19 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.

Setting up and operating or tend machines
This work happens in the physical world: and operating or tend machines, in a real place. Software cannot follow it there.
importance 5 · Core
Source: “Set up and operate or tend machines, such as furnaces, baths, flame-hardening machines, and electronic induction machines, that harden, anneal, and heat-treat metal.” (O*NET task statement)
How this row was scored
Exposure score: 0 out of 100 (0–4 allowing for uncertainty): minimal exposure, high confidence.
Why it sits in this group: work that happens in the physical world.
The rating behind it: Setting up and running furnaces and hardening machines is hands-on equipment work.
The five ratings: output a model can produce 0/4 · needs a body in a room 4/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 2/4.
Instructing new workers in machine operation
This work happens in the physical world: new workers, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Instruct new workers in machine operation.” (O*NET task statement)
How this row was scored
Exposure score: 9 out of 100 (2–16 allowing for uncertainty): minimal exposure, medium confidence.
Why it sits in this group: work that happens in the physical world; the value is that a specific person does it.
The rating behind it: New starters learn machine work by being shown at the machine, though training notes can be drafted.
The five ratings: output a model can produce 2/4 · needs a body in a room 3/4 · needs an accountable person 0/4 · needs to be trusted in the moment 2/4 · how much data exists 2/4.
Adjusting controls to maintain temperatures and heating times
This work happens in the physical world: controls, in a real place. Software cannot follow it there.
importance 5 · Core
Source: “Adjust controls to maintain temperatures and heating times, using thermal instruments and charts, dials and gauges of furnaces, and color of stock in furnaces to make setting determinations.” (O*NET task statement)
How this row was scored
Exposure score: 13 out of 100 (6–20 allowing for uncertainty): minimal exposure, medium confidence.
Why it sits in this group: mistakes that are cheap to catch; work that happens in the physical world.
The rating behind it: Charts and gauges can be read by software, but judging stock color inside a furnace means standing at it.
The five ratings: output a model can produce 2/4 · 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.
Removing parts from furnaces after specified times
This work happens in the physical world: parts, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Remove parts from furnaces after specified times, and air dry or cool parts in water, oil brine, or other baths.” (O*NET task statement)
How this row was scored
Exposure score: 0 out of 100 (0–4 allowing for uncertainty): minimal exposure, high confidence.
Why it sits in this group: work that happens in the physical world.
The rating behind it: Pulling hot parts from a furnace and quenching them is physical work.
The five ratings: output a model can produce 0/4 · needs a body in a room 4/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 2/4.
Moving controls to light gas burners and to adjust gas and water flow and flame temperature
This work happens in the physical world: controls, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Move controls to light gas burners and to adjust gas and water flow and flame temperature.” (O*NET task statement)
How this row was scored
Exposure score: 0 out of 100 (0–4 allowing for uncertainty): minimal exposure, high confidence.
Why it sits in this group: work that happens in the physical world.
The rating behind it: Lighting burners and adjusting gas and water flow is done by hand at the machine.
The five ratings: output a model can produce 0/4 · needs a body in a room 4/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 2/4.
Starting conveyors and open furnace doors to load stock
This work happens in the physical world: conveyors, in a real place. Software cannot follow it there.
importance 5 · Core
Source: “Start conveyors and open furnace doors to load stock, or signal crane operators to uncover soaking pits and lower ingots into them.” (O*NET task statement)
How this row was scored
Exposure score: 0 out of 100 (0–4 allowing for uncertainty): minimal exposure, high confidence.
Why it sits in this group: work that happens in the physical world.
The rating behind it: Opening furnace doors and loading stock is physical work.
The five ratings: output a model can produce 0/4 · needs a body in a room 4/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 1/4.

Show the other 13 tasks

Examining parts to ensure metal shades and colors conform
staying human
This work happens in the physical world: parts, in a real place. Software cannot follow it there.
importance 5 · Supplemental
Source: “Examine parts to ensure metal shades and colors conform to specifications, using knowledge of metal heat-treating.” (O*NET task statement)
How this row was scored
Exposure score: 6 out of 100 (0–13 allowing for uncertainty): minimal exposure, medium confidence.
Why it sits in this group: work that happens in the physical world.
The rating behind it: Judging metal shade and color takes an experienced eye on the actual part.
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 1/4.
Positioning stock in furnaces, using tongs, chain hoists or pry bars
staying human
This work happens in the physical world: stock, in a real place. Software cannot follow it there.
importance 4 · Supplemental
Source: “Position stock in furnaces, using tongs, chain hoists, or pry bars.” (O*NET task statement)
How this row was scored
Exposure score: 0 out of 100 (0–4 allowing for uncertainty): minimal exposure, high confidence.
Why it sits in this group: work that happens in the physical world.
The rating behind it: Positioning stock in a furnace with tongs and hoists is heavy hand work.
The five ratings: output a model can produce 0/4 · needs a body in a room 4/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 1/4.
Loading parts into containers and placing containers on conveyors to be inserted into furnaces
staying human
This work happens in the physical world: parts, in a real place. Software cannot follow it there.
importance 4 · Supplemental
Source: “Load parts into containers and place containers on conveyors to be inserted into furnaces, or insert parts into furnaces.” (O*NET task statement)
How this row was scored
Exposure score: 0 out of 100 (0–4 allowing for uncertainty): minimal exposure, high confidence.
Why it sits in this group: work that happens in the physical world.
The rating behind it: Loading parts into containers and onto conveyors is physical handling.
The five ratings: output a model can produce 0/4 · needs a body in a room 4/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 1/4.
Mounting workpieces in fixtures, on arbors or between centers of machines
staying human
This work happens in the physical world: workpieces, in a real place. Software cannot follow it there.
importance 4 · Supplemental
Source: “Mount workpieces in fixtures, on arbors, or between centers of machines.” (O*NET task statement)
How this row was scored
Exposure score: 0 out of 100 (0–4 allowing for uncertainty): minimal exposure, high confidence.
Why it sits in this group: work that happens in the physical world.
The rating behind it: Mounting workpieces in fixtures is hands-on setup.
The five ratings: output a model can produce 0/4 · needs a body in a room 4/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 2/4.
Reducing heat when processing is complete to allow parts to cool in furnaces or machinery
staying human
This work happens in the physical world: heat, in a real place. Software cannot follow it there.
importance 4 · Supplemental
Source: “Reduce heat when processing is complete to allow parts to cool in furnaces or machinery.” (O*NET task statement)
How this row was scored
Exposure score: 0 out of 100 (0–4 allowing for uncertainty): minimal exposure, high confidence.
Why it sits in this group: work that happens in the physical world.
The rating behind it: Turning the heat down and leaving parts to cool is done at the equipment.
The five ratings: output a model can produce 0/4 · needs a body in a room 4/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 2/4.
Signaling forklift operators to deposit or extract containers of parts into and from furnaces and quenching rinse tanks
staying human
This work happens in the physical world: forklift operators, in a real place. Software cannot follow it there.
importance 4 · Supplemental
Source: “Signal forklift operators to deposit or extract containers of parts into and from furnaces and quenching rinse tanks.” (O*NET task statement)
How this row was scored
Exposure score: 0 out of 100 (0–4 allowing for uncertainty): minimal exposure, high confidence.
Why it sits in this group: work that happens in the physical world.
The rating behind it: Signalling forklift drivers happens on the floor beside the load.
The five ratings: output a model can produce 0/4 · needs a body in a room 4/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 1/4.
Testing parts for hardness, using hardness testing equipment or by examining and feeling samples
staying human
This work happens in the physical world: parts, in a real place. Software cannot follow it there.
importance 4 · Supplemental
Source: “Test parts for hardness, using hardness testing equipment, or by examining and feeling samples.” (O*NET task statement)
How this row was scored
Exposure score: 0 out of 100 (0–4 allowing for uncertainty): minimal exposure, high confidence.
Why it sits in this group: work that happens in the physical world.
The rating behind it: Hardness testing means putting the part in the tester and handling it.
The five ratings: output a model can produce 0/4 · needs a body in a room 4/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 2/4.
Repairing, replacing and maintaining furnace equipment as needed, using hand tools
staying human
This work happens in the physical world: furnace equipment, in a real place. Software cannot follow it there.
importance 4 · Supplemental
Source: “Repair, replace, and maintain furnace equipment as needed, using hand tools.” (O*NET task statement)
How this row was scored
Exposure score: 0 out of 100 (0–4 allowing for uncertainty): minimal exposure, high confidence.
Why it sits in this group: work that happens in the physical world.
The rating behind it: Repairing and maintaining furnace equipment is hands-on work.
The five ratings: output a model can produce 0/4 · needs a body in a room 4/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 2/4.
Heating billets, bars, plates, rods and other stock to specified temperatures preparatory to forging, rolling or processing using oil, gas or electrical furnaces
staying human
This work happens in the physical world: billets, bars, plates, in a real place. Software cannot follow it there.
importance 4 · Supplemental
Source: “Heat billets, bars, plates, rods, and other stock to specified temperatures preparatory to forging, rolling, or processing, using oil, gas, or electrical furnaces.” (O*NET task statement)
How this row was scored
Exposure score: 0 out of 100 (0–4 allowing for uncertainty): minimal exposure, high confidence.
Why it sits in this group: work that happens in the physical world.
The rating behind it: Heating stock in a furnace is physical work with real material.
The five ratings: output a model can produce 0/4 · needs a body in a room 4/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 2/4.
Cleaning oxides and scales from parts or fittings
staying human
This work happens in the physical world: oxides, in a real place. Software cannot follow it there.
importance 4 · Supplemental
Source: “Clean oxides and scales from parts or fittings, using steam sprays or chemical and water baths.” (O*NET task statement)
How this row was scored
Exposure score: 0 out of 100 (0–4 allowing for uncertainty): minimal exposure, high confidence.
Why it sits in this group: work that happens in the physical world.
The rating behind it: Cleaning scale off parts with sprays and baths is physical work.
The five ratings: output a model can produce 0/4 · needs a body in a room 4/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 2/4.
Stamping heat-treatment identification marks on parts
staying human
This work happens in the physical world: heat-treatment identification marks, in a real place. Software cannot follow it there.
importance 3 · Supplemental
Source: “Stamp heat-treatment identification marks on parts, using hammers and punches.” (O*NET task statement)
How this row was scored
Exposure score: 0 out of 100 (0–4 allowing for uncertainty): minimal exposure, high confidence.
Why it sits in this group: work that happens in the physical world.
The rating behind it: Stamping marks onto parts with hammers and punches is hand work.
The five ratings: output a model can produce 0/4 · needs a body in a room 4/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 1/4.
Setting and adjusting speeds of reels and conveyors for prescribed time cycles to pass parts through continuous furnaces
staying human
This work happens in the physical world: speeds of reels, in a real place. Software cannot follow it there.
importance 5 · Supplemental
Source: “Set and adjust speeds of reels and conveyors for prescribed time cycles to pass parts through continuous furnaces.” (O*NET task statement)
How this row was scored
Exposure score: 0 out of 100 (0–4 allowing for uncertainty): minimal exposure, high confidence.
Why it sits in this group: work that happens in the physical world.
The rating behind it: Setting reel and conveyor speeds is done at the machine controls.
The five ratings: output a model can produce 0/4 · needs a body in a room 4/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 2/4.
Mounting fixtures and industrial coils on machines
staying human
This work happens in the physical world: fixtures, in a real place. Software cannot follow it there.
importance 4 · Supplemental
Source: “Mount fixtures and industrial coils on machines, using hand tools.” (O*NET task statement)
How this row was scored
Exposure score: 0 out of 100 (0–4 allowing for uncertainty): minimal exposure, high confidence.
Why it sits in this group: work that happens in the physical world.
The rating behind it: Fitting fixtures and coils with hand tools is physical work.
The five ratings: output a model can produce 0/4 · needs a body in a room 4/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 2/4.

What this job pays, and how many people do it

Median pay: $48,750a 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: 14,000in 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: production schedules in, a record out. The rows above are exactly that shape: reading production schedules and work orders to determine processing sequences and recording times that parts are removed from furnaces to document that objects have attained specified temperatures for specified times. What it cannot do is be there in the room, and that is still where and operating or tend machines get done. 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

Start with what does not change: setting up and operating or tend machines is the middle of this job, and the evidence on this page says it stays with a person.

So, given all that: 7% of this job's task weight sits in rows the software is already learning, 14% in rows that change shape rather than disappear, and 79% in rows it is nowhere near. That is the position, measured across 23 scored tasks. It is not a forecast about you.

So the thing worth your attention is not the job going away. It is the layer around it. Reading production schedules and work orders to determine processing sequences is the part turning into software, and being the person who understands that layer is worth money.

This week: one thing

Ask the one question. Find whoever is bringing new software into your workplace (the manager, the office, whoever runs the system) and ask them what it is meant to do to production schedules, and what it is not meant to touch. Ten minutes, this week, before anyone decides it for you.

What you end up holding: a straight answer about what is actually being rolled out, and when
How long it takes: ten minutes

If there’s nobody obvious to ask, or you’d rather not ask your manager: Put the same question to your union rep, your shift lead or the person who has been there longest, in person, over a break. Same ten minutes, same answer, and you will usually get a straighter one. Write down what they say. The note is the artifact, and it tells you whether and operating or tend machines are in scope or not. Nothing to log into, no license needed.

Over the next 90 days

Get inside the tool rollout rather than waiting for it. Over the next ninety days, ask to be in the group that tests, checks or signs off whatever new system arrives near recording times that parts are removed from furnaces to document that objects have attained specified temperatures for specified times. It is usually an unglamorous seat that nobody fights for, and it is the one that decides how the software is used on your job rather than to it.

Over the next 12 months

On this evidence I would not retrain out of this job, and I will say that plainly rather than hedge it. The task list here is dominated by work that stays with a person. What I would do with a year is get formally recognised for the layer around it (the systems, the compliance, the planning), so you are the one who understands the software instead of the one it is done to. 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 heat treating equipment setters, operators, and tenders, metal and plastic (nearest by the work that AI is not taking, not by job title), and none of them survived. The closest was metal-refining furnace operators and tenders: only about 4% of its durable work is work you already do. And on the numbers you do not need one. This job scores 14/100 here, with only 7% of the task list in the top band, and “set up and operate or tend machines” is not work that hands over cleanly. None of them beats deepening what you already have.

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.

Metal-Refining Furnace Operators and Tenders
Why it looked obvious: It came up as a near neighbour on the work AI is not taking: you already adjust controls to maintain temperatures and heating times, using thermal instruments and charts…, and their equivalent is to regulate supplies of fuel and air, or control flow of electric current and…. Across both published task lists that is about 4% of the durable work in that job.
Why I am not recommending it: Almost none of it is work you already do: about 4% of the durable side of that job. That is a different job, not a next step.
Look at that job’s page anyway →
Maintenance Workers, Machinery
Why it looked obvious: It came up as a near neighbour on the work AI is not taking: you already instruct new workers in machine operation, and their equivalent is to collaborate with other workers to repair or move machines, machine parts, or equipment. 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.
Look at that job’s page anyway →
Welding, Soldering, and Brazing Machine Setters, Operators, and Tenders
Why it looked obvious: It came up as a near neighbour on the work AI is not taking: you already instruct new workers in machine operation, and their equivalent is to give directions to other workers regarding machine set-up and use. 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: 7% of its task weight, across 23 scored tasks. Every row behind it is printed above with the source. Check ours; ignore theirs.
The headlines about your trade disappearing
They are usually about the technology, not the timetable. Changes to work like setting up and operating or tend machines arrive through rules, insurance and money, slowly and visibly. This page tracks the task evidence and will move when it moves.
Retraining out of a job that is holding up
On this evidence I would not spend money leaving. Spend it on the layer around the job instead: the tools, the paperwork, the planning. That is where the change actually is.
The “obvious” next job everyone suggests
I checked the obvious moves and most of them did not survive. The reasons are printed with the routes above, including the pay and the gate. A move that fails on the numbers is worth knowing about so you can stop turning it over at night.

You are reading the United States figures

The United Kingdom splits this work across more than one official group, of which Plastics process operatives 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:

Metal making and treating process operatives

In UK official statistics this job is counted as Plastics process operatives and Metal making and treating process operatives. 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.

Your route through this

Where to go next, and what it costs

Free, and complete

The moves above cost nothing. These are the real services that go with them: public, government-funded, and free at the point of use. Nothing on this page is behind an email address or a payment.

Anywhere in the US:
CareerOneStop - Find local training
Search what's running near you, from the Labor Department's own database, before anyone sells you a course.
Free to search; individual programs vary, and some are funded
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No Space for this job, but one for what is happening to it

Nothing Collab365 runs is built for heat treating equipment setters / operators / tenders / metal / plastic, 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 7% 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 heat treating equipment setters / operators / tenders / metal / plastic. 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 heat treating equipment setters / operators / tenders / metal / plastic 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 heat treating equipment setters / operators / tenders / metal / plastic existed, with researched problems, courses and people in the same boat, would you want in?

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Questions people ask about this job

Will AI replace Heat Treating Equipment Setters, Operators, and Tenders, Metal and Plastic?: Not as a job, but it is already doing parts of the work. Across the 23 official task statements scored for Heat Treating Equipment Setters, Operators, and Tenders, Metal and Plastic (United States, SOC 51-4191), 7% of the importance-weighted core work is made of tasks today's AI could already do most of. The overall exposure score is 14 out of 100 (range 12–19, band: minimal). 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 “Heat Treating Equipment Setters, Operators, and Tenders, Metal and Plastic” can AI already do?: The highest-scoring tasks in release 2026-q4.1 are: “Read production schedules and work orders to determine processing sequences, furnace temperatures, and heat cycle requirements for objects to be heat-treated” (69/100, high); “Determine types and temperatures of baths and quenching media needed to attain specified part hardness, toughness, and ductility, using heat-treating charts…” (56/100, partial); “Determine flame temperatures, current frequencies, heating cycles, and induction heating coils needed, based on degree of hardness required and properties of…” (56/100, partial). 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 “Heat Treating Equipment Setters, Operators, and Tenders, Metal and Plastic” stay human?: About 79% 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: “Mount fixtures and industrial coils on machines, using hand tools” (0/100, minimal); “Set and adjust speeds of reels and conveyors for prescribed time cycles to pass parts through continuous furnaces” (0/100, minimal); “Stamp heat-treatment identification marks on parts, using hammers and punches” (0/100, 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 “Heat Treating Equipment Setters, Operators, and Tenders, Metal and Plastic” do about AI?: Start from the ledger rather than the headline: 7% of this job's weighted core work is exposed, and roughly 79% 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 Heat Treating Equipment Setters, Operators, and Tenders, Metal and Plastic 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 23 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.
1 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.