Remote Mathematics Specialist - AI TrainerSuperAnnotate • Culver City, California, US
Remote Mathematics Specialist - AI Trainer
SuperAnnotate • Culver City, California, US
[job_card.variable_hours_ago]
[job_preview.job_type]
- [job_card.full_time]
- [filters.remote]
[job_card.job_description]You hold BS degree in Math / Stats or a related field in one of the high ranking universities Confident in pure and applied mathematics, proofs, modeling, statistics / probability, and optimization. Expert at fact-checking information using public sources across multiple domains. You communicate mathematics clearly and turn advanced theory into real-world solutions. Background in research, analytical writing, debate, programming, or mathematics is preferred. English proficiency C1 and above with excellent writing skills. Bonus : Data labeling, RLHF, or AI model evaluation experience.
SME Careers, a fast-growing AI data services company and subsidiary of SuperAnnotate, is seeking mathematicians 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 quantitative information across; write high-quality mathematical explanations and model solutions that demonstrate correct methods; and rate and compare multiple AI responses based on mathematical 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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