About Prolific
Prolific is not just another player in the AI space – we are building the biggest pool of quality human data in the world. Over 35,000 AI developers, researchers, and organizations use Prolific to gather data from paid study participants with a wide variety of experiences, knowledge, and skills.
The role
We’re looking for AI Trainer – Machine Learning Specialists to help train and evaluate cutting-edge AI models using real ML expertise. If you have the necessary experience, we’ll send you a quick 10- to 15-minute test to assess your skills and suitability for AI tasks. If successful, you’ll be invited to join Prolific as a participant, where you’ll get paid to train and evaluate powerful AI models. Researchers looking for your skills tend to pay up to $150/hr per AI task completed. You must be prepared to complete paid tasks that require one hour of uninterrupted work, though many are shorter.
What you’ll bring
- AI Training task skills and verifiable professional experience as a Machine Learning Specialist (e.g., ML engineer, data scientist, applied scientist, research engineer)
- A willingness to take our skills verification test to assess your suitability for our participant pool
- Strong attention to detail and the ability to concentrate on complex tasks for up to one hour at a time
- A reliable and fast internet connection and access to a computer
- A willingness to self-declare your earnings, as our participants are self-employed
- A Paypal account to receive payment from our clients
What you’ll be doing in the role
- Completing AI training tasks such as analyzing, editing, and writing annotations (including technical reasoning and structured evaluation)
- Judging the performance of AI in performing ML-relevant tasks (e.g., model/experiment critique, data leakage detection, metrics interpretation, debugging approach, methodology review)
- Improving cutting-edge AI models by providing expert feedback on correctness, robustness, clarity, and technical depth
Key Technologies
- General AI Training
- Model Evaluation and Evals
- Trust and Safety
- Red Teaming
- Quality Analytics
- Data/ML Concepts (e.g., supervised learning, deep learning, NLP, CV, statistics, experimentation)