Machine Feeders and Offbearers

Transportation and Material Moving Occupations

O*NET-SOC code: 53-7063.00

Feed materials into or remove materials from machines or equipment that is automatic or tended by other workers.

Data source: BLSPublished: 2026-08
Low· relative
LowFour relative bandsVery high
Group-level value
Scale, basis and source
Four relative bands (Low / Moderate / High / Very high)
831 detailed occupations in the BLS Employment Projections table. Assigned per National Employment Matrix (NEM) code, so occupations sharing a NEM code carry the same band
Source dataset (XLSX download)
Data source: AnthropicPublished: 2026-03
0.000
0.000Range of values carried here0.745
Scale, basis and source
Observed exposure index, 0–1 as published
Mapped onto O*NET tasks
Published per SOC 2018 occupation; every O*NET occupation with the same SOC 2018 code carries this value
Source dataset (CSV download)
Data source: ILOPublished: 2025
0.12
0.09Range of values carried here0.70
Group-level value
Scale, basis and source
Generative AI exposure index, 0–1 as published
Published per ISCO-08 unit group. Linked to this occupation, wholly or in part, by applying U.S. Bureau of Labor Statistics crosswalks (ISCO-08 to 2010 SOC, 2010 SOC to 2018 SOC) as published
Source dataset (PDF download)

What kind of figure this source publishes

The BLS category is a relative rank, not an absolute level, and it is not a first-hand measurement: it groups an occupation's percentile ranks across several published studies into four bands. It is not an employment or wage forecast, not a probability of adoption, and it does not separate automation from augmentation.

Task-level exposure

Exposed tasks only

13 rated tasks · 1 tasks with β ≥ 0.5 (7.7%)

β = direct exposure (E1) + 0.5 × exposure when tools are available (E2), per the source repository's definition.

Source unit: O*NET 27.2 tasks → O*NET 31.0 occupation code
All rated tasks are in the O*NET 31.0 task list.

Source: OpenAI "GPTs are GPTs" exposure rubric
Release: gh-main-0471612
License: MIT License, Copyright (c) 2024 OpenAI

Values in this tab are predicted labels, not observations. Eloundou et al. (2023) published two rating regimes — human raters and GPT-4 — and the β shown here is derived from the GPT-4 rater basis alone; the same task can take a different value under the other regime. The unit and the meaning differ from the observed shares (%) in the other tabs, so do not place them on the same axis.

Task (O*NET 27.2)	βE1 + 0.5 × E2
Record production and operational data, such as amount of materials processed. O*NET Task ID 14591	1.0
Inspect materials and products for defects, and to ensure conformance to specifications. O*NET Task ID 14583	0.0
Remove materials and products from machines and equipment, and place them in boxes, trucks or conveyors, using hand tools and moving devices. O*NET Task ID 14584	0.0
Push dual control buttons and move controls to start, stop, or adjust machinery and equipment. O*NET Task ID 14585	0.0
Identify and mark materials, products, and samples, following instructions. O*NET Task ID 14586	0.0
Load materials and products into machines and equipment, or onto conveyors, using hand tools and moving devices. O*NET Task ID 14587	0.0
Clean and maintain machinery, equipment, and work areas to ensure proper functioning and safe working conditions. O*NET Task ID 14588	0.0
Weigh or measure materials or products to ensure conformance to specifications. O*NET Task ID 14589	0.0
Fasten, package, or stack materials and products, using hand tools and fastening equipment. O*NET Task ID 14590	0.0
Add chemicals, solutions, or ingredients to machines or equipment as required by the manufacturing process. O*NET Task ID 14592	0.0
Transfer materials and products to and from machinery and equipment, using industrial trucks or hand trucks. O*NET Task ID 14593	0.0
Open and close gates of belt and pneumatic conveyors on machines that are fed directly from preceding machines. O*NET Task ID 14594	0.0
Shovel or scoop materials into containers, machines, or equipment for processing, storage, or transport. O*NET Task ID 14595	0.0

β = E1 + 0.5 × E2 · E1 = tasks where direct LLM access alone cuts time by at least 50%, E2 = tasks where software built on top of an LLM cuts time by at least 50%. Values take only 0 / 0.5 / 1.0.

Data sources & licenses — O*NET®, Eloundou et al. (2023): see full notices on the Credits page

All O*NET 31.0 task statements (13)

All O*NET 31.0 task statements for this occupation (English, as published; task type core or supplemental). Exposure values are not listed here; they are in the table above, next to each task statement as worded in the O*NET version each source used.

Task (O*NET 31.0)	Type
Inspect materials and products for defects, and to ensure conformance to specifications.	Core
Push dual control buttons and move controls to start, stop, or adjust machinery and equipment.	Core
Identify and mark materials, products, and samples, following instructions.	Core
Load materials and products into machines and equipment, or onto conveyors, using hand tools and moving devices.	Core
Clean and maintain machinery, equipment, and work areas to ensure proper functioning and safe working conditions.	Core
Weigh or measure materials or products to ensure conformance to specifications.	Core
Fasten, package, or stack materials and products, using hand tools and fastening equipment.	Core
Record production and operational data, such as amount of materials processed.	Core
Transfer materials and products to and from machinery and equipment, using industrial trucks or hand trucks.	Core
Remove materials and products from machines and equipment, and place them in boxes, trucks or conveyors, using hand tools and moving devices.	Supplemental
Add chemicals, solutions, or ingredients to machines or equipment as required by the manufacturing process.	Supplemental
Open and close gates of belt and pneumatic conveyors on machines that are fed directly from preceding machines.	Supplemental
Shovel or scoop materials into containers, machines, or equipment for processing, storage, or transport.	Supplemental

Anthropic, "What work can robots do?"2026-09

1.39/ 3· Robot exposure index

0 none3 unstructured settings

Scale, basis and source

Each physical task is graded 0–3 by the setting in which robots that have been deployed, sold or demonstrated could do it (0 none · 1 purpose-built robot settings such as a fenced machine cell or a bottling line · 2 structured facilities such as a hospital pharmacy or a container port · 3 unstructured settings such as a private home or a construction site), and the grades are averaged by share of working time

Physical tasks among this occupation's O*NET 29.3 tasks

Anthropic estimated the grades and time shares with Claude Opus 5 using web search, as of 2026.

Source dataset

The robot exposure index is a different measure from the AI exposure figures on this site and cannot be added to or compared with them. It does not measure actual adoption or cost, and 0 also covers occupations with no physical tasks.

Tier of each physical task

Robot environment tiers (E0–E3)

E0 noneDetails
Rated not doable by robots that have been deployed, sold or demonstrated, even in a purpose-built setting
E1 purpose-built robot settingsDetails
Rated doable in a setting built around the machine, such as a fenced machine cell or a bottling line
E2 structured facilitiesDetails
Rated doable in a human work facility that an institution keeps to a working standard, such as a hospital pharmacy or a container port
E3 unstructured settingsDetails
Rated doable in a setting that no institution keeps to a working standard, such as a private home or a construction site

Only tasks classified as physical are listed; other tasks have no tier in the source.

Task (O*NET 29.3)	Robot environment tier
Inspect materials and products for defects, and to ensure conformance to specifications.	E1 purpose-built robot settings
Push dual control buttons and move controls to start, stop, or adjust machinery and equipment.	E1 purpose-built robot settings
Identify and mark materials, products, and samples, following instructions.	E1 purpose-built robot settings
Load materials and products into machines and equipment, or onto conveyors, using hand tools and moving devices.	E2 structured facilities
Clean and maintain machinery, equipment, and work areas to ensure proper functioning and safe working conditions.	E2 structured facilities
Weigh or measure materials or products to ensure conformance to specifications.	E1 purpose-built robot settings
Fasten, package, or stack materials and products, using hand tools and fastening equipment.	E1 purpose-built robot settings
Transfer materials and products to and from machinery and equipment, using industrial trucks or hand trucks.	E2 structured facilities
Remove materials and products from machines and equipment, and place them in boxes, trucks or conveyors, using hand tools and moving devices.	E1 purpose-built robot settings
Add chemicals, solutions, or ingredients to machines or equipment as required by the manufacturing process.	E1 purpose-built robot settings
Open and close gates of belt and pneumatic conveyors on machines that are fed directly from preceding machines.	E2 structured facilities
Shovel or scoop materials into containers, machines, or equipment for processing, storage, or transport.	E2 structured facilities

Employment outlook (BLS)

Employment, 2025: 42K
Projected employment, 2035: 36K
Change (2025–35): -13.1%
Median annual wage, 2025: $41,220
Typical education needed for entry: No formal educational credential

lowest $30,890median wage across 825 detailed occupations, BLS EP 2025–35highest $559,030

Source: U.S. Bureau of Labor Statistics, Employment Projections 2025–35 — Employment by detailed occupation, 2025 and projected 2035

Korea classification links (KSCO)

Links from the current release of the Korea Occupation Direct Crosswalk v1.1, made by AI Changing Work. Relation values are read from the O*NET-SOC occupation side.

KSCO code	KSCO title	Relation	Status
93099	그 외 제조 관련 단순 종사원	This occupation is narrower	confirmed

Relation and status definitions(kr-direct v1.1)

ILO occupational description

AI Changing Work links the ISCO-08 unit groups below to this occupation by applying the U.S. Bureau of Labor Statistics crosswalks (2018 SOC to 2010 SOC, 2010 SOC to ISCO-08) as published, without case-by-case selection; each link may be whole or partial.

ISCO-08 9329Manufacturing Labourers Not Elsewhere Classified

Sources: ILO ISCO-08 — details: Credits

ESCO occupational vocabulary

Each entry below is matched to this occupation in the published ESCO-O*NET Crosswalk (version 1, 2022). The match types are that crosswalk's; the wording is AI Changing Work's.

animal feed operatorESCO 8160.1This occupation is broader
Animal feed operators tend various animal feed processing machines in industrial plants such as mixing machines, filling machines, and loading machines.

Source: European Commission, DG EMPL — ESCO API 2026-08-08 (bridge v1.2.1)

License: CC BY 4.0 (EU Reuse Notice)

View original

ESCO reuse terms and the modification notice are in Credits.

France classification links (ROME)

AI Changing Work's links between this occupation and ROME 4.0 job profiles, judged with AI models. Relations are read from this occupation's side. Links marked “provisional” were adopted on the judgments of two judges from the same model family (Claude) alone, or by an operator rule applied to their judgments, so they rest on weaker evidence than confirmed links.

ROME code	ROME title (French)	Relation
H2205	Opérateur / Opératrice de production en industrie du bois	Related, not a matchprovisional
H2906	Opérateur / Opératrice en fabrication mécanique	Related, not a matchprovisional
H3302	Agent / Agente de conditionnement	Related, not a matchprovisional

Source: France Travail — Répertoire Opérationnel des Métiers et des Emplois (ROME 4.0), version 61, last updated 15 June 2026 (https://www.data.gouv.fr/datasets/repertoire-operationnel-des-metiers-et-des-emplois-rome). Licence Ouverte 2.0.

ROME codes and titles are reproduced unchanged; the links to O*NET-SOC occupations are AI Changing Work's own judgment and are not a France Travail correspondence.

(AI Changing Work link release: fr-direct v1)

Spain classification links (CNO-11)

AI Changing Work's links between this occupation and CNO-11 occupations, judged with AI models. Relations are read from this occupation's side.

CNO-11 code	CNO-11 title (Spanish)	Relation
9700	Peones de las industrias manufactureras	This occupation is narrowerprovisional

Source: Instituto Nacional de Estadística (INE), National Classification of Occupations 2011 (CNO-11, Royal Decree 1591/2010) — www.ine.es · Licensed CC BY 4.0. CNO-11 codes and Spanish titles are reproduced unchanged; the links to O*NET occupations are AI Changing Work's own judgment and are not an INE correspondence.

(AI Changing Work link release: es-direct v1)

Germany classification links (KldB)

AI Changing Work's links between this occupation and KldB occupations, judged with AI models. Relations are read from this occupation's side.

KldB code	KldB title (German)	Relation
24201	Berufe in der Metallbearbeitung (ohne Spezialisierung) - Helfer-/Anlerntätigkeiten	Overlapping, not the same
23101	Berufe in der Papier- und Verpackungstechnik (ohne Spezialisierung) - Helfer-/Anlerntätigkeiten	Overlapping, not the sameprovisional
25101	Berufe in der Maschinenbau- und Betriebstechnik (ohne Spezialisierung) - Helfer-/Anlerntätigkeiten	Overlapping, not the sameprovisional
22101	Berufe in der Kunststoff- und Kautschukherstellung (ohne Spezialisierung) - Helfer-/Anlerntätigkeiten	Related, not a match
29201	Berufe in der Lebensmittelherstellung (ohne Spezialisierung) - Helfer-/Anlerntätigkeiten	Related, not a match

Source: © Statistik der Bundesagentur für Arbeit — Klassifikation der Berufe 2010 – überarbeitete Fassung 2020, https://statistik.arbeitsagentur.de. KldB codes and titles are reproduced unchanged; the links to O*NET occupations are AI Changing Work's own mapping (eigene Zuordnung) and are not a Bundesagentur für Arbeit correspondence.

(AI Changing Work link release: de-direct v1)

Japan classification links (JSCO)

AI Changing Work's links between this occupation and JSCO occupations, judged with AI models. Relations are read from this occupation's side.

JSCO code	JSCO title (Japanese)	Relation
739	その他の運搬・清掃・包装等従事者	Related, not a match

Source: Ministry of Internal Affairs and Communications (MIC), Japan Standard Occupational Classification (Dec. 2009, MIC Notification No. 555). JSCO codes and Japanese titles are reproduced unchanged; the links to O*NET occupations are AI Changing Work's own judgment and are not a MIC correspondence.

(AI Changing Work link release: jp-direct v1)

Sources, licenses and AI Changing Work's modification and translation notices for this page: Credits & sources