I Built a Directory of AI Training Gig Platforms — Here's What I Learned in Week 1

TheAIGigs.com
As a Senior AI Engineer who builds RAG pipelines and agentic systems for a living, I spend a lot of time with models. But I recently started wondering — what if developers and ML practitioners could get paid to interact with AI models, not just build them?
That curiosity led me to build theaigigs.com — a free, no-fluff directory of platforms where you can find legitimate AI training and data annotation gigs. No job board. No AI features. Just a clean, curated list.
Why I Built It
The "AI trainer" economy is quietly exploding. Companies like OpenAI, Anthropic, Google, and hundreds of startups need humans to label and annotate training data, write and rate AI responses (RLHF), test model outputs for accuracy and safety, and create domain-specific datasets.
Platforms like Mercor, Outlier AI, Scale AI, DataAnnotation, Alignerr, Micro1, Appen, and Invisible AI are actively hiring for this kind of work — often remote, flexible, and surprisingly well-paid for the effort involved.
The problem? These platforms are scattered across the internet and hard to discover. Someone has to curate them. So I did.
What's on the Site
The directory currently lists 8 platforms with details on what kind of work each platform offers, skill requirements, how to get started, and what the pay looks like.
There's also a /getting-started guide for people who have never done AI training work before — because the onboarding process varies a lot platform to platform.
Week 1 Traffic Insights (Honest Numbers)
I launched quietly — no Product Hunt, no paid ads. Just a few posts and organic shares. Here's what the first week looked like:
Homepage: 104 visits
/directory: 16 visits
/getting-started: 12 visits
/platforms/mercor: 10 visits
/platforms/alignerr: 5 visits
/platforms/outlier-ai: 4 visits
A few things that surprised me:
36% of visitors were from the US — the highest-value audience for this type of platform.
/getting-started had strong intent. 12 visits in week 1 to a guide page means people aren't just browsing — they want to actually do something with this info.
Mercor dominated platform-specific pages. People are already searching for it — strong brand recognition in this space.
Who This Is For
If you're a developer, ML practitioner, or tech professional with domain knowledge in science, medicine, law, finance, or engineering — and you write clearly, think critically, and want to earn on the side without freelancing full projects — AI training gigs are genuinely worth exploring. Outlier AI and Mercor in particular are known for paying well for expert-level domain work.
The Stack
Keeping it deliberately simple — Vite + React + Tailwind CSS, static data in a single platforms.js file, deployed on Vercel free tier. No database, no auth, no API. The goal was to build something I could launch in a weekend and maintain in minutes per month.
What's Next
Individual platform deep-dive guides, a comparison table with pay rates and skill requirements, and community-submitted reviews for each platform.
Check it out at theaigigs.com. If you've worked on any of these platforms before — Mercor, Outlier, Scale AI, DataAnnotation — I'd love to hear your experience in the comments.
Built by a GenAI engineer who spends his days wiring LangGraph pipelines and occasionally wonders if the AI he's building will eventually come for his own job. Until then — might as well get paid to train it.

Comments

[GoDaddy LLC]

This is a smart example of solving a discovery problem instead of overengineering another “AI-powered AI platform for AI people.” 😄
What I like most is the simplicity of the execution: static pages, clean UX, useful information, and launched fast — proof that momentum still beats architecture astronautics in early-stage products.
The traffic insights are also interesting because they show intent, not just vanity metrics; visits to /getting-started usually mean people are actively looking for income opportunities, not casually scrolling.
I also think the AI training economy is still underestimated — behind every “magical” model response there are thousands of humans doing annotation, evaluation, and RLHF work quietly in the background.
The stack choice deserves respect too: Vite + React + Tailwind + no database is basically the modern version of “ship first, Kubernetes later.” 😂
The funniest part is that we may soon have developers building AI systems during the day and training competing AI models at night like some kind of neural-network moonlighting economy.
Great build overall — practical, focused, and refreshingly honest about the numbers instead of pretending week one traffic broke the internet.