Key findings

There is not enough data in this window to state findings.

Figure 1

How often members qualify

When a member is checked against a role that suits them, how often do they qualify?

Verdict	Share of checks	Share of members
Strong fit	n/a	n/a
Possible fit	n/a	n/a
Unlikely fit	n/a	n/a

Sample. 0 members' fit checks made 1 Oct 2026 to 31 Oct 2026, of 0 members against 0 roles.

A check is one member and one role, judged from the member's profile (their work, never their name or contact details) and the role's own posting: strong, possible or unlikely, with what fits and what is missing. Where a role is closed to the member's country, the verdict is unlikely, decided by the role's own rule.

Checks are made when a member opens a role and asks, or asks for their plan (until 5 Nov 2026 also ahead, for the roles the site listed for each member); so these are roles chosen for each member, not every role.

Members whose best it is: each member counts once, at the best verdict among their checks in the window.

No count below 3 people is shown, nor any share that would reveal one, so no figure can point at a person; a group too small to show on its own is counted in "Other".

Source. AITraining.jobs Research, Do Applicants Qualify for AI Training Roles?, October 2026 edition.

Figure 2

When members ask, and when the site checks ahead

Do members who ask about a role themselves qualify more or less often than for the roles the site picks for them?

No data in this window.

Sample. 0 members' fit checks made 1 Oct 2026 to 31 Oct 2026.

A member asks about a role from its page on the site; the site checks ahead the roles it lists for the member (their matches and their plan).

Source. AITraining.jobs Research, Do Applicants Qualify for AI Training Roles?, October 2026 edition.

Figure 3

How often members qualify, by kind of work

For which kinds of AI training work do the people looking for it most and least often qualify?

No data in this window.

Sample. 0 members' fit checks made 1 Oct 2026 to 31 Oct 2026, by the kind of work of the role.

Each role is classified into one of the kinds of AI training work this site tracks, from its title and description.

No row with any checks is a small sample: the rows differ so much that each reads on its own from its first.

Source. AITraining.jobs Research, Do Applicants Qualify for AI Training Roles?, October 2026 edition.

Figure 4

How often members qualify, by the field a role asks for

For roles in which professions and fields do applicants most and least often qualify?

No data in this window.

Sample. 0 members' fit checks made 1 Oct 2026 to 31 Oct 2026, by the field the role's title names.

A role is in a field when its title names one of the field's professions, by the rule the site's profession pages list roles with; a role naming two fields counts in both, and one naming none (most general rating, writing and language roles) is in the last row.

No row with any checks is a small sample: the rows differ so much that each reads on its own from its first.

Source. AITraining.jobs Research, Do Applicants Qualify for AI Training Roles?, October 2026 edition.

Figure 5

How often members qualify, by the credential they hold

Do people with a licence, a doctorate or a master's qualify more often for AI training work?

No data in this window.

Sample. The 0 members with a fit check made 1 Oct 2026 to 31 Oct 2026.

With a strong fit: the share of the group's members with at least one strong verdict among their checks in the window.

A credential is what the member stated on the site, else what they gave the candidate pool, else what their profile shows.

No row with any members is a small sample: the rows differ so much that each reads on its own from its first.

No count below 3 people is shown, nor any share that would reveal one, so no figure can point at a person; a group too small to show on its own is counted in "Other".

Source. AITraining.jobs Research, Do Applicants Qualify for AI Training Roles?, October 2026 edition.

Figure 6

How often members qualify, by their own field

Which fields' professionals most often find AI training work they strongly qualify for?

No data in this window.

Sample. The 0 members with a fit check made 1 Oct 2026 to 31 Oct 2026.

A member's field is the field of the best fit their profile reading names.

No row with any members is a small sample: the rows differ so much that each reads on its own from its first.

No count below 3 people is shown, nor any share that would reveal one, so no figure can point at a person; a group too small to show on its own is counted in "Other".

Source. AITraining.jobs Research, Do Applicants Qualify for AI Training Roles?, October 2026 edition.

Figure 7

What members most often lack

When a member falls short of an AI training role, what does the role ask for that their background does not show?

The role asks for	Share of checks	Of unlikely fits	Of possible fits	Of strong fits
Experience of AI training or annotation work	n/a	n/a	n/a	n/a
A degree	n/a	n/a	n/a	n/a
Years or seniority of experience	n/a	n/a	n/a	n/a
Expertise in a profession or field	n/a	n/a	n/a	n/a
A language, or a level of it	n/a	n/a	n/a	n/a
A professional licence or certification	n/a	n/a	n/a	n/a
Something else the role asks for	n/a	n/a	n/a	n/a
Where they live, may work or when they can work	n/a	n/a	n/a	n/a
A named tool or programming skill	n/a	n/a	n/a	n/a

Sample. All 0 members' fit checks made 1 Oct 2026 to 31 Oct 2026, with the gaps the judge wrote.

Each check's gaps are the judge's own lines on what the role asks for and the member's background does not show. A check counts once for each kind its gaps name; a check with no gap counts for none, so the kinds do not add up to 100%.

A gap is read with the requirement report's own patterns (degrees, credentials, years, skills), the language list of the languages report, the professions of the profession pages, the programming languages and tools of the programming-languages report, and three phrasings only a gap uses: AI training or annotation work, a span or rank of experience, and where or when a member can work.

Patterns: AI training work \b(annotat\w*|label(?:l)?ing|label(?:l)?ers?|ai[- ]?(?:train\w*|tutor\w*|data|evaluat\w*|rater|model)s?|llms?|rlhf|content moderat\w*|moderation|raters?|prompt\w*|transcri\w*|evaluating (?:ai|models?|llms?))\b; experience (\b\d{1,2}\s*\+?\s*(?:[-–]\s*\d{1,2}\s*)?(?:years?|yrs?)\b|\b(?:one|two|three|four|five|six|seven|eight|nine|ten)(?:[- ]plus)?\s+(?:or more\s+)?years?\b|\b(?:senior|seniority|mid[- ]?senior|mid[- ]level|track record|proven|professional experience|years of)\b); where and when \b(not open in|open only|only open|based in|us[- ]based|resid\w*|located|location|time ?zones?|availability|available|full[- ]time|part[- ]time|hours? (?:a|per) week|on[- ]?site|in[- ]person|work authori[sz]ation|visa|citizen\w*|us[- ]specific|country|countries)\b.

Source. AITraining.jobs Research, Do Applicants Qualify for AI Training Roles?, October 2026 edition.

Figure 8

What members lack, by kind of work

Which kinds of AI training work most often ask for a field's expertise, a language, a degree or experience the applicant does not have?

No data in this window.

Sample. All 0 members' fit checks made 1 Oct 2026 to 31 Oct 2026, with the gaps the judge wrote.

Shares of each kind of work's own sampled checks.

No row with any sampled checks is a small sample: the rows differ so much that each reads on its own from its first.

Source. AITraining.jobs Research, Do Applicants Qualify for AI Training Roles?, October 2026 edition.

Figure 9

The languages members most often lack

Which languages do AI training roles ask for that the people applying do not show?

No data in this window.

Sample. All 0 members' fit checks made 1 Oct 2026 to 31 Oct 2026, with the gaps the judge wrote. 0 of the sampled checks name a language in a gap.

A gap names a language by the words the languages report reads in role titles. Where a role needs a language the member shows no sign of speaking, the judge's verdict is unlikely, whatever the rest of their background.

No row with any sampled checks is a small sample: the rows differ so much that each reads on its own from its first.

Source. AITraining.jobs Research, Do Applicants Qualify for AI Training Roles?, October 2026 edition.

Figure 10

The expertise members most often lack

In which fields do AI training roles ask for expertise the people applying do not show?

No data in this window.

Sample. All 0 members' fit checks made 1 Oct 2026 to 31 Oct 2026, with the gaps the judge wrote. 0 of the sampled checks name a field's expertise in a gap.

A gap names a field when it names one of the field's professions, by the rule the profession pages list roles with (a gap asking for 'legal domain expertise' names law).

Source. AITraining.jobs Research, Do Applicants Qualify for AI Training Roles?, October 2026 edition.

For labs, data vendors and researchers

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The figures above are a summary. Behind them are records we collect ourselves, every day:

0 roles tracked, each with its history
Every role on 0 job boards, read since n/a (every hour since 18 Oct 2026): its pay and place, and the first and last day it was open.
0 apply clicks in 28 days
Which roles people actually apply to, by field, pay and kind of work. It happens on this site, so nobody else can count it.
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The job boards of 25 AI labs and 14 human-data vendors, read every day: who is hiring for human data, and who is hiring to buy it.

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Method and definitions

The profiles of the people looking for AI training work on AITraining.jobs (their LinkedIn profile, CV or own words, read by the site), the credentials they state or their profile shows, and the job alerts they subscribe to. Each fit check reads a member's profile beside a role's own posting. Every figure is computed by one database query over the window shown, the same query for the live report and for every edition. Each figure states its sample beneath it.

Member: A person with a profile on AITraining.jobs by the window's end: someone looking for AI training work who gave the site their LinkedIn profile, their CV or their own description of their work.
Fit check: One member against one role: the site reads the member's profile (their work, never their name or contact details) beside the role's own posting and answers strong, possible or unlikely, with what fits and what the role asks for that the background does not show. Checks are made when a member asks about a role or for their plan (until 5 Nov 2026 also ahead, for the roles the site listed for each member).
Gap: A line of the judge's on what the role asks for and the member does not show, read with the requirement report's own patterns, the languages report's words and the professions of the profession pages. A gap can be of several kinds.
Gap sample: What members lack is read from a simple random sample of the window's checks, 38,415 of them: the fewest for a share printed to a whole percent to be within half a point of the whole window's, 19 times in 20. A window with fewer checks is read whole.
Field: A member's field is the field of the best fit their profile reading names (software and IT, health and medicine, law, languages and translation, and the other groups the site's matching uses). A role is in a field when its title names one of the field's professions, by the rule the profession pages list roles with.
Highest credential: A professional licence (to practise as a physician, nurse, lawyer, pharmacist, psychologist, accountant, engineer and the like), else a research doctorate, else a master's degree, else none: what the member stated on the site, else what they gave the candidate pool, else what their profile shows. Not known when none of these says.
No cell points at a person: No count of fewer than 3 people is published, nor any share that would reveal one: with 3 or more, no one, not even a person counted, can tell who the others are. When one hidden cell could be worked out from a row's total, a second is hidden too.
Window: Live: checks made in the last 28 complete days, in UTC. Edition: one calendar month.

What this data cannot tell you

These are the people who use AITraining.jobs, not every person who does AI training work: the site's audience leans to the countries and fields it is found in.
A profile reading can miss what a profile does not say, and a credential nobody states is Not known, never guessed.
The roles checked are the ones that suit each member, so a share is of the work people would apply to, not of every role.
A verdict is a reading of what the profile and the posting say, not a platform's decision: a platform can accept someone the judge called a possible fit, or turn down a strong one.
A check made again for a newer judge replaces the earlier one, so a past window counts the verdicts as they now stand.

How the job feeds are read and roles are classified: site methodology. How fresh, complete and consistent the data is, checked every day: data quality and coverage.

Editions

The live report covers the last 28 complete days and changes every day. Each complete month is also published as an edition, frozen two days after the month ends, so a figure you cite from an edition never moves.

Live dataLast 28 complete days, recomputed daily
October 2026 edition (this page)Frozen 11 Nov 2026

Cite and reuse

Free to quote, chart and republish under CC BY 4.0 (creativecommons.org/licenses/by/4.0) with a link to this page. This edition is frozen, so its figures will read the same later.

Citation

AITraining.jobs (2026). Do Applicants Qualify for AI Training Roles?, October 2026 edition. https://cyberjobs.cc/research/do-applicants-qualify-for-ai-training-roles/2026-10

BibTeX

@misc{aitrainingjobs_do_applicants_qualify_for_ai_training_roles_2026_10,
  author = {{AITraining.jobs}},
  title = {Do Applicants Qualify for AI Training Roles?, October 2026 edition},
  year = {2026},
  howpublished = {\url{https://cyberjobs.cc/research/do-applicants-qualify-for-ai-training-roles/2026-10}},
}

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