Key findings

1
Within the same kind of work, roles open worldwide drew 2.3 times the apply clicks per open role of roles open only to listed countries, the strongest association measured (12 kinds of work compared).
Figure 1
2
Roles with 'freelance' in the title drew 97% fewer apply clicks per open role than roles without it in the same kind of work, the weakest association measured.
Figure 1
3
Pooled across kinds of work, roles with 'freelance' in the title drew 98% fewer apply clicks per open role than roles without it, but within the same kind of work 97% fewer clicks per open role: they are concentrated in kinds of work that draw fewer applicants per role.
Figure 1
4
Roles in the top fifth of their kind of work's advertised rates drew 0.64 times the clicks per open role of their kinds' average, against 1.01 for the bottom fifth and 1.19 for roles stating no rate.
Figure 2
5
In their first week, roles drew 0.69 times the clicks per open role of their kind of work's average; listed more than three months earlier, 1.10 times, a gap that partly reflects the site's own recency ranking.
Figure 3
6
Roles stating an hourly rate drew more apply clicks per open role than roles stating none in 9 of the 12 kinds of work compared; among the kinds whose two sides read on their own, the ratio ran from 7.05x in Red-Teaming & AI Safety to 0.36x in Data Annotation & Labeling.
Figure 5
7
Roles open worldwide drew 0.69 times the clicks per open role of their kind of work's average, and roles open only to US residents 0.12 times.
Figure 4

Figure 1

What goes with more applicants, compared within each kind of work

Which traits of an AI training role draw more apply clicks per open role than similar roles without them?

Trait	Within kind (with / without)	All kinds pooled	Roles with the trait	Kinds of work compared	Compared with
Open worldwide	2.30x	2.25x	1,544	12	Roles open only to listed countries
Pay in the top third of its kind	1.30x	1.29x	1,931	12	Roles in the bottom third
Listed less than 14 days earlier	0.91x	0.89x	1,843	12	Roles listed before that
Title names an expert field	0.87x	0.82x	3,490	12	Roles whose title names none
States an hourly rate	0.80x	0.72x	5,809	12	Roles that state none
Title names a language	0.66x	0.58x	2,730	11	Roles whose title names none
Title of 9 words or more	0.27x	0.29x	544	11	Roles with 4 words or fewer
Open only to US residents	0.14x	0.18x	1,641	12	Roles open worldwide
Title says freelance	0.03x	0.02x	681	9	Roles without it

Sample. 53,718 apply clicks on 7,140 roles listed at some point from 1 Oct 2026 to 31 Oct 2026, over 15,771 open role-weeks.

Rows are marked, and not ranked in the findings, when the two sides' rates do not read on their own: each side's clicks per open role-week leans on the two sides' pooled rate by the weight fitted on their own clicks, and a row is read only when both sides' open role-weeks reach that weight (two sides whose rates differ no more than chance never do). The index figures read each row against their own rows the same way.

Within kind: over the kinds of work with open time on both sides, the clicks the roles with the trait drew against the clicks they would have drawn at their kinds' own rates, over the same for the roles without it (each kind's rate taken over both sides). 1.00x is no difference; a trait needs two such kinds of work. Pooled: the same ratio with every kind of work together. Where the two disagree, the trait sits in kinds of work that draw more (or fewer) applicants per role.

Pay thirds are formed within each kind of work, from the roles listed in the window that state an hourly rate (ties share a third). A listing's age is taken at the middle of the days it was open in the window; roles with no known listing date are left out of that comparison. Expert fields and languages are read from the title with the same vocabularies as the expert-fields and languages reports.

Title words often follow a platform's posting template ('freelance', for example, appears in the titles of a handful of platforms), so within a kind of work a title trait also compares the platforms that use it with those that do not.

Part of every association here is this site's own ranking: the default order puts recently listed roles, roles that state their pay and roles at partner networks higher, and shows each visitor the roles open in their country first. A ratio describes the people who use this site, not a cause.

Clicks on a platform's general sign-up link (5,429 in the window) have no role and are left out, as are 76 clicks on roles with no open time in the window (seen only once, or removed before it).

Source. AITraining.jobs Research, What Draws Applicants to AI Training Roles, October 2026 edition.

Figure 2

Applicants by pay level, within each kind of work

Do better-paid roles draw more applicants than other roles of the same kind?

Pay level within its kind of work	Within-kind index	Clicks per role a week	Median rate, per hour	Roles	Open role-weeks	Clicks	Clicks at its kinds' rates
Lowest fifth of its kind's rates	1.01x	3.38	$20	1,091	2,481	8,380	8,257.7
Second fifth	0.66x	2.20	$37	1,259	2,925	6,432	9,754.7
Middle fifth	1.14x	3.55	$60	1,162	3,134	11,136	9,728.3
Fourth fifth	1.29x	4.53	$80	1,068	2,329	10,541	8,186.7
Highest fifth	0.64x	2.27	$115	1,229	2,049	4,657	7,263.9
No hourly rate stated	1.19x	4.41	n/a	1,331	2,853	12,572	10,526.7

Sample. Roles listed from 1 Oct 2026 to 31 Oct 2026 in the 12 kinds of work with at least 5 roles stating an hourly rate (one for each fifth) (7,140 roles, 53,718 apply clicks).

Within-kind index: the clicks the group drew over the clicks it would have drawn at the average rate of its own kinds of work. 1.00x is that average; the rows of this figure add up to the same clicks observed and expected.

Fifths are formed within each kind of work from the advertised rates of its roles listed in the window, each role once, ties sharing a fifth; the median rate is across kinds, for scale.

Rows with fewer than 13.7 expected clicks are marked as a small sample and not ranked: below that the average of the rows weighs more than a row's own expected clicks (the weight fitted on the rows' own counts).

Source. AITraining.jobs Research, What Draws Applicants to AI Training Roles, October 2026 edition.

Figure 3

Applicants by listing age, within each kind of work

How much more do new listings draw than older roles of the same kind?

Listing age	Within-kind index	Clicks per role a week	Roles	Open role-weeks	Clicks	Clicks at its kinds' rates
First week	0.69x	2.19	1,444	1,151	2,525	3,634.4
Second week	1.33x	4.75	399	836	3,971	2,980.3
Weeks 3 and 4	1.03x	3.99	593	1,344	5,363	5,207.5
1 to 3 months	1.08x	3.60	1,880	4,366	15,701	14,578.3
More than 3 months	1.10x	3.69	2,201	6,872	25,345	23,042.6
Listing date unknown	0.19x	0.68	623	1,202	813	4,274.9

Sample. 53,718 apply clicks on 7,140 roles listed at some point from 1 Oct 2026 to 31 Oct 2026, over 15,771 open role-weeks.

A listing's age is taken at the middle of the days it was open in the window, from its listing date (the job board's posting date, or else its first day in the feed).

Within-kind index: clicks over the clicks expected at the average rate of the role's own kind of work; the rows add up to the same clicks observed and expected.

Recency is a lever of this site's own default order, which puts recently listed roles first, so this figure reads the site's ranking as much as applicants' preference. A newer role is also more likely to be one people have not applied to yet.

Rows with fewer than 3.3 expected clicks are marked as a small sample and not ranked: below that the average of the rows weighs more than a row's own expected clicks (the weight fitted on the rows' own counts).

Source. AITraining.jobs Research, What Draws Applicants to AI Training Roles, October 2026 edition.

Figure 4

Applicants by who may apply, within each kind of work

Do roles open worldwide draw more applicants than country-limited roles of the same kind?

Location rule	Within-kind index	Clicks per role a week	Roles	Open role-weeks	Clicks	Clicks at its kinds' rates
Open worldwide	0.69x	2.44	1,544	3,061	7,457	10,754.4
Worldwide except listed countries	3.84x	13.12	988	2,706	35,495	9,255.3
Only the United States	0.12x	0.43	1,641	2,986	1,295	11,037.4
Only other listed countries	0.42x	1.36	2,937	6,968	9,459	22,459.3
No rule stated	0.06x	0.24	30	50	12	211.6

Sample. 53,718 apply clicks on 7,140 roles listed at some point from 1 Oct 2026 to 31 Oct 2026, over 15,771 open role-weeks.

Only the United States: a rule that names the United States and no other country. Only other listed countries: every other include-list, whatever the countries.

Within-kind index: clicks over the clicks expected at the average rate of the role's own kind of work.

Each visitor is shown the roles open in their own country first, so a country-limited role is ranked first only for visitors from its countries.

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

Source. AITraining.jobs Research, What Draws Applicants to AI Training Roles, October 2026 edition.

Figure 5

Stating the pay, kind by kind

In which kinds of work do roles that state an hourly rate draw more applicants than roles that do not?

Kind of work	With / without	Clicks per role a week, rate stated	No rate stated	Roles with / without	Clicks with / without
Red-Teaming & AI Safety	7.05x	1.65	0.23	81 / 10	265 / 5
Writing & Content	3.69x	5.14	1.39	70 / 18	658 / 49
Robotics & Physical-AI Data	2.91x	1.14	0.39	108 / 44	254 / 13
Research Studies & Surveys	2.78x	0.85	0.30	34 / 24	36 / 19
Math & STEM	2.78x	3.14	1.13	294 / 50	2,700 / 92
Law, Medicine & Finance	2.77x	3.35	1.21	2,174 / 276	15,300 / 603
Agentic Tasks & RL Environments	2.16x	1.39	0.64	143 / 43	344 / 45
AI Evaluation & Rating (RLHF)	1.22x	4.32	3.54	311 / 125	2,490 / 495
Coding & Software EvaluationSmall sample	1.05x	3.71	3.52	425 / 89	3,926 / 541
Multilingual & Localization	0.72x	1.91	2.64	1,305 / 285	5,803 / 2,220
Transcription, Audio & Speech	0.71x	3.39	4.80	621 / 264	5,032 / 3,318
Data Annotation & Labeling	0.36x	8.21	22.92	243 / 103	4,338 / 5,172

Sample. Roles listed from 1 Oct 2026 to 31 Oct 2026 in the 12 of 12 kinds of work whose sides stating and not stating an hourly rate both read on their own.

Rows whose two sides do not read on their own are marked (each side's open role-weeks must reach the weight of the two sides' pooled rate, fitted on their own clicks): a few popular roles can move them.

Whether a role states its pay is mostly set by the platform's posting template, so within a kind of work this also compares platforms with one another.

Source. AITraining.jobs Research, What Draws Applicants to AI Training Roles, October 2026 edition.

For labs, data vendors and researchers

Buy the data behind this report

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

11,482 roles tracked, each with its history
Every role on 62 job boards, read since 13 Jul 2026 (every hour since 18 Oct 2026): its pay and place, and the first and last day it was open.
58,632 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.
39 lab and vendor hiring boards
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.

What you can buy

The records
The rows behind any figure, with their history, not only the totals.
A regular feed
The same figures every week or month, as files.
A custom cut or benchmark
Your countries, languages or fields, or your own platform against the market.

Building AI and need the experts behind this data? (you contract them yourself) or (we deliver the expert work).

Investing in AI-data companies? Hiring radar is these records role by role, every day, as files.

Method and definitions

Apply clicks made on AITraining.jobs, joined to the role clicked and the days each role was open, with each role's pay, location rule, listing date and title from the public job boards this site reads every day. 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.

Apply click: A click on an apply button on AITraining.jobs that opens a platform's application page for one role. Made by a person: automated traffic is set aside as it is recorded. Clicks on a platform's general sign-up link have no role and are left out.
Open role-weeks: For every role, the days of the window it was listed, divided by 7 and summed. Only roles listed at some point in the window count.
Clicks per open role a week: Apply clicks divided by open role-weeks.
Within-kind ratio: For each kind of work with open time both with and without the trait (and a click without it), clicks per open role a week with the trait over the same rate without it, each side leaning on its kind's pooled rate by the weight fitted for the trait on every kind's two sides (a kind with little open time reads close to no difference); then the average across those kinds, weighted by the open role-weeks with the trait. A trait needs two such kinds. 1.00x is no difference.
Pooled ratio: The same ratio with every kind of work together. Where it differs from the within-kind ratio, the trait is concentrated in kinds of work that draw more or fewer applicants per role.
Within-kind index: For a group of roles, the apply clicks they drew over the clicks they would have drawn at the average rate of their own kinds of work. 1.00x is that average.
Pay thirds and fifths: Formed within each kind of work from the advertised hourly rates of its roles listed in the window, each role once, ties sharing a band. Fifths need at least 25 priced roles in the kind.
Listing age: Days from a role's listing date (the job board's posting date, or else its first day in the feed) to the middle of the days it was open in the window. A new listing is under 14 days old.
Location rule: Read from each posting's location text and, where that names no country, from what the posting's own description states: open worldwide, worldwide except listed countries, only listed countries (US-only when the United States is the one country), or no rule stated.
Expert field, language, freelance: Named in the title, matched as whole words against the vocabularies of the expert-fields, languages and job-titles reports.
Kind of work: Each role is classified from its title and description into one of the twelve kinds of AI training work this site tracks. Every kind links to its own page.
Window: Live: the last 28 complete days, in UTC. Edition: one calendar month, in UTC.

What this data cannot tell you

Part of every association is this site's own ranking: the default order puts recently listed roles, roles that state their pay and roles at partner networks higher, and each visitor sees the roles open in their country first. The listing-age, pay and location figures read that ranking as much as applicants' preferences.
A ratio is an association among the people who use AITraining.jobs, not a cause: traits travel together (a new role is often also worldwide), and the comparison holds only the kind of work fixed.
Title words often follow a platform's posting template, so within a kind of work a title trait also compares the platforms that use it with those that do not.
Clicks per role are few (well under one a week on average), so a ratio can move with a handful of popular roles from one window to the next; read a trait across editions before relying on it.
An apply click starts an application. It is not a submitted application, an acceptance or a hire, and one person can click several roles.
Platforms that recruit without a public job board are not counted.

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)Freezes on first reading
September 2026 editionFrozen 10 Oct 2026
August 2026 editionFrozen 10 Oct 2026

Cite and reuse

Free to quote, chart and republish under CC 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). What Draws Applicants to AI Training Roles, October 2026 edition. https://aitraining.jobs/research/what-draws-applicants-to-ai-training-roles/2026-10

BibTeX

@misc{aitrainingjobs_what_draws_applicants_to_ai_training_roles_2026_10,
  author = {{AITraining.jobs}},
  title = {What Draws Applicants to AI Training Roles, October 2026 edition},
  year = {2026},
  howpublished = {\url{https://aitraining.jobs/research/what-draws-applicants-to-ai-training-roles/2026-10}},
}

Data: every figure as CSV (one line per value) and JSON, sent to your work email.Pay and open-roles data

More research

All 81 research reports