AI Job Types: Find Your Fit
From data annotation to domain expertise — which AI gig role matches your skills?
AI companies hire for dozens of different roles, each requiring different skills and paying different rates. Some need technical expertise, others need subject-matter knowledge, and some just need attention to detail.
Here's the breakdown of the most common roles, what they pay, and how to land them.
🎯 RLHF Trainer / AI Evaluator
What you do: Rate, rank, and improve AI-generated responses. You're teaching the AI what "good" looks like by comparing outputs and explaining your preferences.
Typical tasks:
- Compare two AI responses and pick the better one
- Edit AI outputs to make them more accurate/helpful
- Write "golden responses" for the AI to learn from
- Spot factual errors, bias, or unsafe content
Best for: Generalists with strong communication skills. No specialized domain knowledge required, but clear reasoning is essential.
Where to find it: Mercor, Scale AI, Appen
🧠 Domain Expert
What you do: Apply your professional expertise to train AI in your domain. Doctors train medical AI, lawyers train legal AI, engineers train coding AI, etc.
Typical tasks:
- Review AI outputs in your field for accuracy
- Write expert-level responses to complex questions
- Create training data for specialized use cases
- Validate AI performance on domain-specific tasks
Best for: Licensed professionals (doctors, lawyers, CPAs) and subject-matter experts with provable credentials. This is where the real money is.
Where to find it: Mercor, Turing, direct contracts with AI labs
🔴 AI Red Teamer
What you do: Try to break the AI. Your job is finding edge cases, jailbreaks, harmful outputs, and safety vulnerabilities before real users do.
Typical tasks:
- Craft prompts that make AI behave badly
- Test for bias, hallucinations, and harmful outputs
- Document security vulnerabilities
- Find ways to bypass safety filters
Best for: People who think like hackers. Background in cybersecurity, QA testing, or adversarial thinking is a plus.
Where to find it: Mercor, direct AI lab postings (OpenAI, Anthropic, etc.)
💻 Software Engineering Expert
What you do: Help train AI coding assistants by writing better code, reviewing AI-generated code, and explaining software engineering concepts.
Typical tasks:
- Review and improve AI-generated code
- Write clean, well-documented code examples
- Explain algorithms and system design
- Test coding AI for bugs and performance
Best for: Software engineers with 3+ years of experience. The better your coding skills, the higher the pay.
Where to find it: Mercor, Turing, Scale AI
🏷️ Data Annotator / Labeler
What you do: Label, categorize, or tag data (images, text, audio) so AI can learn from it. This is the entry-level option — repetitive but accessible.
Typical tasks:
- Draw bounding boxes around objects in images
- Classify text into categories
- Transcribe audio or label sentiment
- Tag content for moderation (NSFW, violence, etc.)
Best for: Entry-level, no special skills required. Good for students or anyone looking for flexible side income.
Where to find it: Scale AI, Appen, Remotasks, Amazon MTurk
✍️ Prompt Engineer
What you do: Design, test, and optimize prompts to get better outputs from AI models. You're an AI whisperer.
Typical tasks:
- Craft effective prompts for specific use cases
- Test prompt variations and measure results
- Build prompt libraries for companies
- Teach others how to use AI tools effectively
Best for: Power users of ChatGPT, Claude, or Midjourney. If you know how to coax great outputs from AI, this is your niche.
Where to find it: Freelance platforms (Upwork, Toptal), direct consulting
📊 Data Scientist / ML Engineer
What you do: More technical than other roles — you're working closer to the models themselves. Requires ML knowledge.
Typical tasks:
- Build datasets for training
- Evaluate model performance
- Run experiments and A/B tests
- Improve model accuracy through data quality
Best for: People with ML/data science background. You need to understand models, not just use them.
Where to find it: Turing, Toptal, direct AI lab contracts
How to Choose the Right Role
Have a professional credential (MD, JD, PhD)? → Domain Expert
Strong coder? → Software Engineering Expert
Think like a hacker? → Red Teamer
Good at explaining things? → RLHF Trainer
Just getting started? → Data Annotator
AI power user? → Prompt Engineer
Pay Ranges: What to Expect
Entry-level ($15-30/hr): Data annotation, basic labeling, no expertise required
Mid-level ($40-80/hr): RLHF training, general evaluation, some expertise helpful
Expert-level ($80-200/hr): Domain experts (doctors, lawyers, specialists), red teaming, senior engineers
Your actual rate depends on:
- Your credentials — Proven expertise = higher pay
- The platform — Some pay more than others
- Your quality score — High-quality work unlocks better projects
- Demand — Hot skills command premium rates
Can You Do Multiple Roles?
Absolutely. Many contractors juggle several types of work:
- A software engineer might do coding work + RLHF training
- A doctor might do medical domain work + red teaming
- A teacher might do education-focused evaluation + prompt engineering
Diversifying makes you more resilient when one type of work slows down.
What's the Hardest to Get Into?
Easiest: Data annotation — low barrier, lots of openings
Hardest: Domain expert roles requiring rare credentials (e.g., board-certified specialists)
Red teaming is also competitive because it requires a unique skill set and companies hire selectively.
Next Steps
- Identify your strengths — What's your expertise?
- Pick 2-3 role types that match your skills
- Browse current openings and apply
- Start with one platform, build your reputation, then expand