data
Posted Jan 8AI Training - Machine Learning Specialist (UK)
at Prolific
Remote
Responsibilities
- Model Evaluation and Evals • Trust and Safety • Red Teaming • Quality Analytics •
Requirements
- *]:pointer-events-auto scroll-mt-[calc(var(--header-height)+min(200px,max(70px,20svh)))]" data-turn-id="7d8c6042-ac58-4acb-b90b-d08242002c4b" data-testid="conversation-turn-6" data-scroll-anchor="true" data-turn="assistant"> *]:pointer-events-auto scroll-mt-[calc(var(--header-height)+min(200px,max(70px,20svh)))]" data-turn-id="788a8b78-cfcd-42e1-beb1-30da5949a95d" data-testid="conversation-turn-8" data-scroll-anchor="true" data-turn="assistant"> AI Trainer – Machine Learning Specialists 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) •
- Strong attention to detail and the ability to concentrate on complex tasks for up to one hour at a time •
- 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 •