Most people approach AI training jobs in the wrong way.
They either focus only on high-paying platforms or give up too early.
From my experience, a simple three-step strategy works much better.
1. Don’t Ignore Smaller Platforms
At the beginning, it’s a mistake to focus only on top companies.
Smaller platforms — such as Innodata or similar — often pay less, but they are easier to access.
These platforms are important because they help you:
• build initial experience
• understand how tasks work
• create a basic track record
Even a small amount of work is useful. Over time, this becomes part of your resume and makes it easier to move forward.
2. Apply to Larger Platforms (Even Early)
At the same time, you should not wait too long before applying to larger companies.
Platforms like Mercor or Micro1 are more selective, but they offer better long-term opportunities.
A good approach is to apply to these platforms even with generalist roles.
You don’t need to be highly specialized at the beginning — getting access is the first step.
3. Move to Domain-Specific Roles
Once you gain some experience, the next step is specialization.
This is where the real improvement in pay and quality of work happens.
You should focus on roles related to your background, for example:
• engineering
• medical
• legal
• finance
Domain-specific roles are harder to enter, but they usually offer higher pay and more stable opportunities.
Final Thought
This process takes time.
You start with smaller platforms, build experience, move to larger companies, and then specialize.
It’s not a single step — it’s a progression.
Those who follow this path usually achieve better results over time.
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