aitrainer.work AI training jobs

搜尋來自 20 多個平台的開放 AI 訓練與 RLHF 職缺，包含薪資、符合資格的國家與招聘統計資料。免費、無需 API 金鑰、唯讀的遠端伺服器。

託管 MCP 伺服器

npx add-mcp 'https://aitrainer.work/mcp'

可安裝到 Claude Code、Codex、Cursor 等客戶端

文件

The State of the AI Training Job Market

Monthly headline figures, frozen per edition: AI Training Pay Index

$67.5/hr

median of 5,645 listed rates since Dec 2025, relistings counted once

Open listings today: $70/hr

46.8 days

median listing lifespan, all listings

~30 days

median time from referral to hire (details)

Cite this dataset

Data from <a href="https://aitrainer.work/open-data">aitrainer.work</a>, licensed under <a href="https://creativecommons.org/licenses/by/4.0/">CC BY 4.0</a>.

Data from aitrainer.work/open-data, licensed under CC BY 4.0 (creativecommons.org/licenses/by/4.0)

Romeo, P. (2026). AI Training Labor Market Data: Pay, Listing Volume and Hiring Outcomes [Data set]. aitrainer.work. https://doi.org/10.5281/zenodo.23042213

For papers, cite the DOI. Each monthly version is archived on Zenodo, and the CSVs are also on Hugging Face. CC BY 4.0 — free to reuse with attribution. Full license and full dataset download: see License and Download below.

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Media kit ↗

Median hourly pay by field

USD, hourly-priced listings only

Legal

$102

n=186

Healthcare

$100

n=260

Finance

$100

n=140

Psychology

$83

n=39

Business

$80

n=653

Accounting

$80

n=60

STEM

$75

n=469

Humanities

$72.5

n=82

Design

$71.8

n=56

Creative

$70

n=11

Median hourly pay by platform

USD, hourly-priced listings only

Remo Experts

$95

n=19

AfterQuery

$90

n=191

Handshake

$85

n=248

Ethos

$80

n=465

Mercor

$75

n=1840

Terac

$70

n=34

Micro1

$55

n=1853

Mindrift

$55

n=52

Vetto

$53.5

n=26

DataAnnotation

$50

n=11

Ethos: Expert network placing senior and PhD-level consultants with third-party clients. Rates reflect consulting work and are not directly comparable to crowdsourced AI-training platforms.

Listing volume by field

Total listings observed, all platforms

Generalist

1,085

n=1085

Languages

1,035

n=1035

Business

808

n=808

Software Engineering

800

n=800

STEM

748

n=748

Healthcare

340

n=340

Data Science

301

n=301

Legal

212

n=212

Finance

212

n=212

Writing

204

n=204

New listings per month

Excludes each platform's onboarding backfill

Feb Mar Apr May Jun Jul Aug Sep Oct 418

All figures report sample size (n). Breakdowns with fewer than 10 listings, or for pay fewer than 10 distinct rates, are hidden. See Methodology.

Talent supply vs. job demand by track (n=1,425 candidates)

Candidates in the aitrainer.work talent pool, grouped into the same 10 tracks used to classify job listings, compared against total listings observed per track. Tracks with more listings than candidates indicate a sourcing gap. Tracks with more candidates than listings indicate higher competition per opening.

Business & Finance

Candidates

267

Listings

1,097

Generalist

Candidates

414

Listings

1,086

Languages & Translation

Candidates

103

Listings

1,035

Technical / Software Engineering

Candidates

231

Listings

896

Science & STEM

Candidates

111

Listings

833

Writing, Humanities & Creative

Candidates

Listings

411

Healthcare & Psychology

Candidates

Listings

388

Data Science & Analytics

Candidates

Listings

301

Legal

Candidates

Listings

212

Voice Acting & Audio

Candidates

Listings

121

Candidates are classified automatically from parsed CV text, or from structured skills and occupation where no CV is on file, using the same keyword taxonomy applied to job listings. Candidates matching no track are counted as Generalist. Aggregate counts only. No names, email addresses, or other identifying fields are included.

Referral outcomes and time to hire

What happened after aitrainer.work referred 6,000+ candidates to 7 AI training platforms. The sections above report advertised listing terms. Figures here are pooled across platforms and rounded.

400+

candidates hired

~7%

pooled placement rate (a lower bound)

~30 days

median referral to hire

Cumulative hires by time since referral

Share of hires with a known hire date, rounded to 5 points

Within 2 weeks

~25%

Within 6 weeks

~65%

Within 3 months

~80%

Per-platform figures are not published. Referral volume and follow-through vary by platform, so a per-platform number reflects our referral process as much as the platform.

Companies dataset

How 60 companies that pay people to train AI hire, pay and accept contributors. Every fact links to the company page we checked it on.

Download the full dataset

One JSON file holds every figure on this page, plus monthly pay, contract length and weekly hours. You can download or link it freely, with no sign-up or API key.

https://aitrainer.work/data/market-trends.json Download ↓

For citations, use the dated monthly snapshot. Snapshots are frozen once their month closes: https://aitrainer.work/data/market-trends/market-trends-2026-10.json

Live job listings API

The open listings behind the job board, as JSON. No sign-up or API key. Responses refresh about once an hour and fall under the same CC BY 4.0 license as the dataset.

GET https://aitrainer.work/api/jobs.json

Parameter	What it does
platform	Platform key, e.g. `mercor` or `sme-careers`
domain	Field, e.g. `Mathematics` or `Software Engineering`
limit	Jobs per page, 1 to 500 (default 100)
offset	Where the page starts. Each response gives `total` and `next_offset`
seen_within_days	Only listings we saw open in the last N days. Some platforms are checked less often than daily, so `last_seen` is on every job
compact	`1` leaves out the long descriptions

Listings are newest first. A job's pay_source of estimate means the platform published no rate and the figure is ours. The whole board is also one file at /data/jobs.json (gzipped at /data/jobs.json.gz).

Machine-readable spec (OpenAPI 3.1): /openapi.json.

If you show these listings, credit aitrainer.work with a link, using the attribution below.

Ask your AI assistant about AI training jobs

Add our MCP server to Claude, ChatGPT, Cursor or any assistant that supports MCP, and it can search open AI training roles and pay for you. No sign-up or key.

https://aitrainer.work/mcp

match_jobs_to_profile ranks open roles for one person from their profession, skills, languages, country and minimum pay. Nothing about the person is stored.
search_jobs finds open roles by keyword, platform, field, country or minimum hourly pay, most relevant first.
get_job returns one role in full: requirements, hours, interview and the countries it accepts.
get_interview_prep explains how a platform hires and gives practice questions for a role, for a mock interview.
list_platforms compares platforms by open roles, median pay and payout speed.
get_pay_stats gives market pay and the hiring numbers from the monthly Pay Index.

In Claude, open Settings, then Connectors, choose "Add custom connector" and paste the URL. In Claude Code, run claude mcp add --transport http aitrainer-work https://aitrainer.work/mcp.

License: CC BY 4.0

Free to use for commercial and non-commercial research, reporting, or applications. Attribution with a link to this page is required.

The license covers every file and endpoint on this page: /data/market-trends.json, /data/companies.json, /data/companies.csv, the jobs feed at /data/jobs.json and /data/jobs.json.gz, and the /api/jobs.json API.

Attribution

Data from <a href="https://aitrainer.work/open-data">aitrainer.work</a>, licensed under <a href="https://creativecommons.org/licenses/by/4.0/">CC BY 4.0</a>.

Data from aitrainer.work/open-data, licensed under CC BY 4.0 (creativecommons.org/licenses/by/4.0)

Full license: creativecommons.org/licenses/by/4.0

Methodology & limitations

Corpus

All listings observed since tracking began, including listings that have since closed. The dataset has no active/expired split, because that split depends on the build date.

First-seen dates

The date a listing was first observed by this project, which can be later than the platform's publication date. Listings already open when tracking began on a platform are dated to their first observation. Each platform's first month of coverage includes a backlog of listings already open at onboarding. These months are flagged includes_backfill: true and should be excluded from growth comparisons.

Pay figures

Hourly-priced listings only, in USD. Where a listing advertises a range, the midpoint is used. Each distinct rate counts once: a role relisted on the same platform with the same title and rate is one data point, and sample_size counts distinct rates, not listings. Excluded: rates below $3/hr or above $400/hr (parsing errors), max-only rates where the platform publishes only a ceiling, open-ended placeholder ranges whose top is more than 10 times the bottom, per-task, monthly and annual pay, platform-wide disclosure bands, and any rate that is our estimate rather than one the listing states, including rows saved before estimates were tagged. platform_balanced_median_hourly_usd is the median of per-platform medians (platforms with at least 10 distinct rates), because one platform supplies about half of all distinct rates.

Fields

Field (domain) is assigned from listing title, description, and platform tags. Approximately one third of listings have no confident field assignment and are excluded from field breakdowns. Field names used by older releases of the classifier (for example Law, Biology, Voice) are folded into the current ones (Legal, STEM, Voice Acting).

Listing lifespan

Days between first and last observation of a listing. Listings still open at generation time are excluded, which biases the median slightly downward.

Sample suppression

Breakdowns with fewer than 10 listings are suppressed.

Known limitations

Coverage is limited to tracked platforms and does not represent the full market. Pay disclosure varies by platform, so per-platform sample sizes vary widely. Read each median alongside its sample_size.

Referral outcomes: scope

Outcomes for candidates aitrainer.work referred to AI training platforms since March 2026, pooled across platforms and rounded. Counts are floored (referrals to the nearest 1,000, placements to the nearest 50), the rate is a whole percent, and time figures are rounded to 5 days and 5 percentage points.

Referral outcomes: time to hire

Days from a candidate's first recorded referral to the platform's confirmed hire date, pooled across platforms that report a hire date for each candidate. Reported as a median to limit the influence of outliers. Most dated hires come from one platform, so the figure mostly reflects that platform's timing.

Referral outcomes: placement rate

Placements divided by candidates referred, pooled across all tracked partners. A referral is an introduction, and many referred candidates never complete the partner's application, so this rate describes the referral funnel and should be read as a lower bound. It does not represent any individual applicant's probability of being hired.

Referral outcomes: known limitations

Platform-reported and refreshed periodically, so figures rise as outcomes are recorded. Hires not attributed back to aitrainer.work are not captured, which biases the rate downward. Per-platform figures are not published.

Listing data is sourced from the aitrainer.work AI training job board. Press and data requests: [email protected].