Learn to do AI-training work well.
Build the skills to evaluate AI responses, explain your decisions and produce work that holds up in review.
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Try five free samplesWhat the work looks like now
Every open listing we track, classified by the kind of work it asks for. Each distinct ad counts once, so a provider posting the same ad in fifty cities does not skew it. An ad can ask for several kinds of work, so the shares add up to more than 100%.
| Work | Share of ads | New ads / older | Median stated rate | In the program |
|---|---|---|---|---|
| Multilingual / translation | 43.6% | 37.8% / 48.7% | $28/hr | Multilingual track |
| Expert domain (STEM, medical, legal, finance) | 19.1% | 23.2% / 16.6% | $99/hr | Expert task and verifier |
| Audio / speech | 16.4% | 18.2% / 12.9% | $31/hr | Not yet |
| Evaluating AI responses | 11.6% | 9.9% / 10.3% | $65/hr | Side-by-side evaluation |
| Coding / software engineering | 8.1% | 6.5% / 5.9% | $75/hr | Coding and agentic track |
| Rubrics / grading | 6.5% | 13.8% / 4% | $70/hr | Rubric grading and writing |
| Prompt writing / red-teaming / safety | 4.3% | 3.9% / 3.6% | $60/hr | Not yet |
| Agents / computer use | 3.8% | 14.3% / 1% | $73/hr | Agent trajectory review, Trajectory capture |
| Search / ads / maps rating | 3.2% | 3.3% / 3.8% | $22/hr | Not yet |
| Side-by-side / preference ranking | 2.8% | 3.9% / 3% | $18/hr | Side-by-side evaluation |
| Image annotation | 2.5% | 3.6% / 2.6% | $65/hr | Image annotation |
| Video / egocentric / robotics | 1.9% | 2.6% / 1.6% | $24/hr | Multimodal and field capture track |
As of October 16, 2026: 3,480 distinct ads from 7,755 open listings. "New ads / older" compares the share of ads first seen in the last 30 days with the share among older ads. The rate is the median of distinct stated hourly rates in US dollars; listings that state no rate are left out, and a rate is shown only when at least 5 distinct rates support it.
How the program is organized
Six units. Every lesson is free and every claim in it links to its source. Graded practice sits beside the unit it belongs to. 19 of the 19 lessons are live and side-by-side evaluation is in an invite-only beta; everything else below is planned.
- Unit 1
Foundations
How human work becomes model training data, how that work is checked, and the rules that get contributors removed.
- How human data trains AI modelsFree lesson, live
- How your work is checked: gold tasks, calibration and agreementFree lesson, live
- What gets contributors removedFree lesson, live
- Unit 2
Evaluation
Judging model responses: side-by-side preference, written justifications, rubrics and factual checks.
- Judging two responses side by sideFree lesson, live
- Writing justifications that hold up in reviewFree lesson, live
- Rubrics: writing criteria and grading against themFree lesson, live
- Checking a response for factual errorsFree lesson, live
- Side-by-side evaluationGraded practice, invite-only beta; free samples live
- Rubric grading and writingGraded practice, planned; free samples live
- Unit 3
Agentic work
Agents that act on computers: what a trajectory is, how demonstrations are recorded, how runs are graded, and how tasks are made hard to game.
- What an agent trajectory isFree lesson, live
- Recording computer-use demonstrationsFree lesson, live
- Grading an agent run step by stepFree lesson, live
- Tasks, verifiers and reward hackingFree lesson, live
- Agent trajectory reviewGraded practice, planned
- Trajectory captureGraded practice, planned
- Unit 4
Multimodal and physical
Annotating images, capturing video and first-person footage for robotics, and the consent questions recording raises.
- Image annotation conventionsFree lesson, live
- Video and egocentric captureFree lesson, live
- Consent and privacy when you recordFree lesson, live
- Image annotationGraded practice, planned
- Unit 5
Expert work
For people with a degree or a profession: writing hard tasks in your field and grading expert work without knowing who produced it.
- Writing hard prompts an expert can verifyFree lesson, live
- Grading expert work blindFree lesson, live
- Expert task and verifierGraded practice, planned
- Unit 6
Getting in and getting paid
Which network fits your background, what each screening evaluates according to public sources, and how pay and tiers work once you are in.
- Which network fits your backgroundFree lesson, live
- What each screening evaluates, from public sourcesFree lesson, live
- How pay and tiers workFree lesson, live
Pick a route
Routes through the units for different starting points. The figures under each are the job ads in our data that ask for that kind of work.
First-time generalist
You have no AI-training experience yet and want to know what the work is, how it is checked and which network to apply to first.
Route: Foundations, Getting in and getting paid, Evaluation; practice: Side-by-side evaluation, Rubric grading and writing
464 ads from 20 networks, median stated rate $65/hr.
Generalist response rating is a shrinking share of new listings in our data, so this track ends with the rubric work that carries higher stated pay.
Domain expert
You have a degree or professional experience in a field such as medicine, law, finance, science or engineering.
Route: Foundations, Evaluation, Expert work, Getting in and getting paid; practice: Side-by-side evaluation, Rubric grading and writing, Expert task and verifier
665 ads from 19 networks, median stated rate $99/hr.
Expert listings carry the highest stated pay in our data and are a growing share of new listings.
Coding and agentic
You write software, or you are comfortable checking what an AI agent did on a computer, step by step.
Route: Foundations, Evaluation, Agentic work, Getting in and getting paid; practice: Rubric grading and writing, Agent trajectory review, Trajectory capture
380 ads from 22 networks, median stated rate $75/hr.
Agent and computer-use work is the fastest-rising work type in our data, from a small base.
Multilingual
You are fluent in a language other than English and want evaluation work in it.
Route: Foundations, Evaluation, Getting in and getting paid; practice: Side-by-side evaluation, Rubric grading and writing
1,517 ads from 22 networks, median stated rate $28/hr.
Language work is the largest category by number of listings and the lowest paid by stated hourly rate in our data.
Multimodal and field capture
You want image, video or recording work, including filming everyday tasks for robotics data.
Route: Foundations, Multimodal and physical, Getting in and getting paid; practice: Image annotation
143 ads from 18 networks, median stated rate $36/hr.
Image annotation is a small and flat share of listings; first-person video capture is small but rising.
How practice is scored
Every graded item comes from an openly licensed public dataset labeled by people, named on the item with its license. No model writes the answer key.
| Module | What you do | How it is scored | Ads asking for it |
|---|---|---|---|
| Side-by-side evaluation | Compare two model responses to the same prompt, rate your preference on a 7-point scale, mark the reasons and write a justification. | Agreement within one point of HelpSteer3's human reference label, the original annotators' average rounded with ties to even. The report compares your agreement with individual annotators to their agreement with each other and shows guessing baselines. Items where annotators preferred opposite responses are marked as split and excluded from agreement and completion counts. | 464 (13.3%) |
| Rubric grading and writing | Grade responses against published, expert-written rubric criteria, then write a rubric of your own for a new prompt. | Introductory grading compares your decision with the original physician labels. Items physicians split on are unscored. Rubric-writing practice and its reviewed scoring design are still in development. | 227 (6.5%) |
| Agent trajectory review | Read a recorded agent run step by step, find the first step that went wrong, classify the error and judge whether the task succeeded. | Scored against the verdict of the public benchmark the run comes from, which checks success automatically. | 131 (3.8%) |
| Trajectory capture | Complete a task in a mock app inside the page while every action is logged with a one-line reason, the way computer-use demonstrations are recorded. | A deterministic checker scores the end state and the trajectory. Nothing you do leaves your browser except the score. | 131 (3.8%) |
| Image annotation | Draw and label bounding boxes on images from a public dataset, following written labeling rules. | Scored on overlap with the reference boxes, missed objects, wrong labels and the labeling rules (tight boxes, occlusion). | 86 (2.5%) |
| Expert task and verifier | Write a hard task in your own field together with a check that can verify the answer, then test whether a model could game it. | Scoring design will be developed once the earlier practice modules have real learner data to calibrate against. | 665 (19.1%) |
What this is not
- Not a requirement
- No AI-training network requires this program, and the free lessons cover what you need to know to apply anywhere. The FTC's advice on job scams is plain: "Don't pay for the promise of a job." Paying for practice here promises you no job at any network.
- Not a credential
- Finishing a module gives you a completion record for your own use. No network recognizes it, and we will not suggest that one does.
- Not a provider's screening
- We never reproduce a network's screening questions or the project guidelines its contributors work under. Lessons that describe a screening say only what the network itself has published, with a link.
- Not scored by a model
- Your answers are compared with labels people made, and your agreement is shown beside how often the original annotators agreed with each other. Where they split, we say so rather than marking you wrong.
- Your answers and your choices
- Ordinary practice answers are stored to score them and show your progress. They are not sold or used to train a model. If optional contributions are offered later, training, commercial licensing and publication will need separate permissions. Declining will not affect your access or scores. Earlier work stays protected unless you explicitly authorize its reuse. Read the privacy policy and terms of use.
- Not finished
- 19 free lessons are live. Side-by-side evaluation is in an invite-only beta. The other lessons and practice modules are planned, not live. The order they ship in follows the data above and can change with it.
Why join the waitlist instead of waiting for launch
Graded practice will be paid; lessons stay free. Your price is locked in before the public one applies, whatever that ends up being.
Practice modules open to the waitlist first, in signup order, and are free while each is in beta.
Which routes and modules come first is shaped by who is actually on the list, alongside the job-ad data.
Be first in when practice opens
Meanwhile, read the free lessons, no signup needed: start with the first one. The free tools are live now: browse the full network directory, check what these roles actually pay, or see open roles today.
Frequently asked questions
Is HumanSourcer Prep available yet?
Partly. 19 of the 19 free lessons are live, covering Foundations and Evaluation and Agentic work and Multimodal and physical and Expert work and Getting in and getting paid, and anyone can read them without signing up. Graded practice for side-by-side evaluation is in an invite-only beta: invites go to the waitlist in signup order, and it is free while in beta. The other practice modules are still in development and will open the same way.
What will it cost?
The lessons are free and will stay free. Graded practice will be paid, and its price is not set yet. Waitlist members get founding-member pricing locked in before the public price applies.
Will finishing it get me hired?
No, and nothing here should be read as saying so. No AI-training network requires this program or recognizes it. It explains how the work is done and checked, and lets you practice it against data labeled by people, so you can apply knowing where your judgment stands. Hiring decisions are the network's alone.
Do I need experience with AI training or data annotation work?
No. There are routes for people applying to their first network and for people with a profession or a degree who want the better-paid expert work. Pick the route that matches your background.
How do you decide what to teach first?
From the job ads themselves. Every night we classify each open listing we track by the kind of work it asks for, counting each distinct ad once so a provider posting the same ad in fifty cities does not skew it. As of October 16, 2026 that is 3,480 distinct ads from 7,755 listings. The modules ship in order of demand and pay, weighed against how reliably we can score them.
How is this different from the free provider directory?
The free directory (76 networks) tells you what exists and what each network is known to pay. HumanSourcer Prep covers the work itself: how evaluation, rubric, agent and annotation tasks are done, how they are checked, and how your answers compare with trained human annotators.
When does it launch?
No fixed date for the whole program. Graded practice for side-by-side evaluation opened as an invite-only beta in October 2026. Each lesson ships when every claim in it has a source we have read, and each practice module ships when its scoring data has a license we have checked. Waitlist members hear first, in the order they signed up.