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Plant and machine operators, and assemblers · code 8141

Rubber products machine operators

Rubber products machine operators monitor and operate machines which knead and blend rubber and rubber compounds and produce various components and products from natural and synthetic rubber, such as moulded footwear, domestic articles, insulating materials, industrial accessories or tyres. Tasks include - (a) operating and monitoring machines which knead, mix and blend rubber and rubber compounds for further processing; (b) operating and monitoring machines which produce sheets of rubber or rubberized fabric by a rolling process; (c) operating and monitoring machines which extrude compounded rubber or shape vulcanized rubber by moulding; (d) operating and monitoring machines which build up tyres on a form, vulcanize tyres and mould or rebuild used tyres; (e) examining outputs for defects and conformity to specifications; (f) locating defects and repairing worn and faulty tyres by vulcanizing or other processes. Examples of the occupations classified here: - Latex worker - Rubber extruding machine operator - Rubber milling machine operator - Rubber moulder - Rubber products machine operator - Tyre maker - Tyre repairer - Vulcanizer

None of this job's tasks scored high enough to count. It ranks 120 of 427 for holding up against AI.

Also known as

  • rubber products machine operator

Job titles from the EU's ESCO list. Search for any of them on the jobs page and you'll end up here.

81.8
Holds up
against AI Not Exposed

AI could take on 18.2 of 100, on average
so it holds up at 100 − 18.2 = 81.8

That's the whole sum, and you can check it against the ILO's study.

69.5
How hands-on
out of 100

From O*NET, averaged across 8 US jobs , so treat it as rough

42.2
How fiddly
and cramped

finger skill 45.3 · hand skill 46.1 · cramped spaces 35.2 · averaged

Kind of hands-on work · hands-on 50+ · fiddly 39.4+ Hands-on, fiddly work in tight spaces

More fine handwork, and more time in cramped or awkward spots, than a typical hands-on job. That is exactly what machines find hardest: they do best in tidy, predictable places.

estimated O*NET 31.0 BLS occupational crosswalk chain ISCO-08 to 2010 SOC (Aug 2012, rev. Jun 2015); 2010 to 2018 SOC (Nov 2017); O*NET-SOC 2019 taxonomy data 0.7-work-setting measures chosen by Frey & Osborne (2017) · the grouping is a sorting, not a measurement

This number is probably too high

This score only looks at AI tools like chatbots. It doesn't look at robots at all — and this job rates 69.5 out of 100 for how hands-on it is, which is exactly where robots come in.

Have a look at the tasks below and judge for yourself. The ILO scored them low because a chatbot can't do them. Whether a machine with hands could is a different question, and we can't answer it yet.

The tasks behind the score

The score is the average of these. A job is really a bundle of tasks, and an average can hide how uneven that bundle is.

The task AI could do most of scores 27.3, while the average is 18.2. A big gap means some of this job is wide open to AI and some of it barely at all. That's quite different from a job where everything sits somewhere in the middle, even when the averages match.

  1. 27.3

    Examining outputs for defects and conformity to specifications

    Low
  2. 18

    Operating and monitoring machines which build up tires on a form, vulcanise tires and mould or rebuild used tyres

    Very Low
  3. 16.5

    Operating and monitoring machines which knead, mix and blend rubber and rubber compounds for further processing

    Very Low
  4. 16.5

    Operating and monitoring machines which produce sheets of rubber or rubberized fabric by a rolling process

    Very Low
  5. 16

    Operating and monitoring machines which extrude compounded rubber or shape vulcanized rubber by moulding

    Very Low
  6. 15.1

    Locating defects and repairing worn and faulty tyres by vulcanizing or other processes.

    Very Low

6 tasks · the High to Very Low labels are the ILO's

from the source Working Paper 140, Generative AI and Jobs 2025 index data 0.7-work-setting

Helped, or replaced?

The score above can't tell a tool that makes you faster from one that does the task instead of you. Jobs and Skills Australia looked at those two things separately, and this is their result, matched to this job.

One Australian job matches this one closely, so this is about as reliable as it gets here.

AI helps you
58
AI does it for you
26

Helping wins by 32 points.

estimated Our Gen AI Transition (Gen AI Capacity Study) September 2025 detailed data release data 0.7-work-setting matched to Australian jobs 7115

Compared with similar jobs

There are 39 jobs in the “Plant and machine operators, and assemblers” group, averaging 80.1. This one is above that.