Remote Chemist - AI TrainerSuperAnnotate • Warren, Michigan, US
Remote Chemist - AI Trainer
SuperAnnotate • Warren, Michigan, US
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[job_card.job_description]You hold an MS or PhD in Chemistry or a related field in a high ranking university. Confident in chemical reactions and stoichiometry, organic mechanisms, analytical methods, and physical / quantum chemistry. Expert at fact-checking information using public sources across multiple domains. You communicate chemistry clearly and turn advanced theory into real-world solutions. English proficiency C1 and above with excellent writing skills. Background in research, analytical writing, debate, programming, or chemistry is preferred. Bonus : Data labeling, RLHF, or AI model evaluation experience. You enjoy applying chemical reasoning to evaluate and improve AI capabilities. For PHD holders : you have developed or critically reviewed complex chemistry content (e.g., problem banks, proofs, textbook sections, research notes).
We’re seeking chemists to join our AI training project. In this role, you will review AI-generated responses, evaluate the reasoning quality and step-by-step problem-solving, and provide expert feedback that helps models produce answers that are accurate, logical, and clearly explained. You will assess solutions for accuracy, clarity, and adherence to the prompt; identify errors in calculations, methodology, or conceptual understanding; fact-check chemical information across; write high-quality chemical explanations and model solutions that demonstrate correct methods; and rate and compare multiple AI responses based on chemical correctness and reasoning quality.
Key Responsibilities :
- Develop AI Training Content : Create detailed prompts in various topics and responses to guide AI learning, ensuring the models reflect a comprehensive understanding of diverse subjects.
- Optimize AI Performance : Evaluate and rank AI responses to enhance the model's accuracy, fluency, and contextual relevance.
- Ensure Model Integrity : Test AI models for potential inaccuracies or biases, validating their reliability across use cases.
Your Profile :
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Chemist • Warren, Michigan, US
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