How Software Engineers Can Earn Money Training AI Models

AI companies need large amounts of carefully judged data to train and evaluate models that write code. A handful of platforms recruit developers as independent contractors to produce that data: reviewing AI-generated code, comparing solutions and writing explanations. This article explains how that arrangement works and what to weigh before you apply.

Short version: it can be flexible, remote, project-based work that uses skills you already have. It is not guaranteed income, platforms decide who they accept, and the amount of work available can change at any time.

Why the work exists

Models that write code improve through feedback. Some of that feedback is automated — running tests, for example — but much of it needs human judgement:

• Is this explanation accurate?
• Did the response follow every instruction, or only the ones a test checks?
• Which of two correct answers is more useful to the person who asked?

Those questions need someone who can read code. That is why platforms recruit developers rather than general annotators for coding projects.

How the platforms work

The platforms we cover sit between AI companies and contributors. Broadly:

1. You apply on the platform's website, often by creating a profile or résumé.
2. You qualify through the platform's own process. Depending on the platform this can include a starter assessment, a domain-specific test, an AI-led interview and identity verification.
3. You are matched to projects if accepted. Projects have their own instructions and quality standards.
4. You are paid by the platform as a contractor, typically per hour or per task.

Some specifics, from the platforms' own public pages (checked 12 October 2026):

• DataAnnotation asks applicants to choose a qualification track and take a Starter Assessment that, according to its FAQ, can only be taken once.
• Alignerr describes applying to individual job listings, phone and ID verification, and — for some projects — an AI interview or domain-specific assessment.
• Mercor documents an AI interview, assessments and ID verification, with pay set per contract.

See the full comparison for sources.

What do platforms say about pay?

Platforms publish their own ranges, and they differ by role. As one example, DataAnnotation's Software Engineer listing states "up to $40–$150+/hr depending on task difficulty and specialization" (checked 12 October 2026). Other platforms show pay on individual listings or contracts.

Treat these as the platform's description of its rates — not a forecast of what you will earn. Several things sit between a published rate and an actual payment:

• Acceptance. Not every applicant passes the assessment.
• Availability. DataAnnotation's FAQ, for example, says availability "depends on both the demand for your skills and your past performance on the platform".
• Quality. Projects review work; low-quality work can reduce future access.
• Location and payments. Some listings are region-restricted, and payment methods vary by country.
• Taxes and status. You are usually an independent contractor, responsible for your own taxes.

A simple, honest way to think about it

If you want to model scenarios, multiply an hourly rate you consider realistic by hours you can actually commit — then treat the result as an upper bound, not an expectation. Our illustrative calculator does exactly that and nothing more.

What makes a strong applicant

From the requirements platforms publish, recurring themes are:

1. Reading code you didn't write, quickly and accurately.
2. Reasoning about correctness, including edge cases and failure modes.
3. Writing clearly — your explanation is often the product.
4. Following detailed instructions precisely.
5. Comfort in at least one mainstream language; DataAnnotation's listing names Python, C++, Rust, Go and JavaScript as the most common in its tasks.

A degree is not always required. DataAnnotation's coding listing explicitly says "No degree required" (checked 12 October 2026).

Trade-offs to weigh

Potential upsides

• Remote, often flexible hours.
• Uses skills you already have.
• Exposure to how AI models are evaluated, which is relevant to many engineering roles.
• Sharpens code-review and written reasoning skills.

Real downsides

• No guarantee of acceptance or of steady work.
• Work can be repetitive and quality-checked closely.
• Contractor status: no employment benefits, and tax admin is yours.
• Check your employment contract: some employers restrict outside work or have conflict-of-interest rules.

Avoiding scams

The popularity of this work attracts imitators. Practical rules:

• Apply on the platform's official website. Our platform guides link to each official application page.
• Be wary of anyone who asks you to pay to be hired, or promises acceptance.
• Never share passwords or one-time codes.
• Legitimate platforms may ask for identity verification as part of onboarding; do it through the platform, not through a third party's link.

Our own paid products are optional training, are not required by any platform, and cannot get you accepted.

Getting started

1. Answer the questionnaire to see which platforms' published requirements match your background.
2. Practise with the free Code Comparison lab.
3. Read each platform's guide and apply directly on its website when ready.

Independent guide. Not affiliated with any platform. Earnings vary and are not guaranteed. Some links may be referral links — see our Affiliate Disclosure.