Most people approach AI training jobs 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 and similar — often pay less, but they're easier to access. They matter because they help you build initial experience, understand how tasks work, and create a basic track record. Even a small amount of work is useful; over time it becomes part of your résumé and makes it easier to move forward.
2. apply to larger platforms early
At the same time, don't wait too long before applying to larger companies. Platforms like Mercor and Micro1 are more selective but offer better long-term opportunities. A good approach is to apply to them even for 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. Focus on roles related to your background — engineering, medical, legal, finance. Domain-specific roles are harder to enter, but they usually offer higher pay and more stable opportunities.
the short version
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. The people who follow this path tend to get better results over time.