Data annotation: Accurately label and categorize mathematical expressions, equations, proofs, word problems, and other relevant data.
Concept mapping: Connect mathematical concepts and establish relationships between different areas of mathematics to help AI models understand the underlying structure of the subject.
Problem-solving verification: Analyze AI-generated solutions to mathematical problems, identifying errors and providing feedback to improve model accuracy.
Curriculum development: Contribute to the development of comprehensive training datasets that cover a wide range of mathematical concepts and difficulty levels.
You’re a great fit if
You bring deep expertise in mathematics (research, teaching, or applied problem‑solving)
You’ve built or reviewed complex data, educational, or technical content
You communicate concepts clearly and enjoy turning theory into practice
Bonus: Experience with data labeling, RLHF, or other AI training projects
About the role
Flexible workload — work from anywhere, on your own schedule
High impact — your craft directly improves models used by top AI labs & Fortune 500 teams
Clear ownership — know exactly what success looks like and have autonomy to deliver
Growth potential — consistent high performers spearhead new programs and mentor incoming SMEs
Interview process
Complete a screening with Zara, our AI interviewer in English, to learn more about your background and experience.
Domain-specific Zara interview to assess your understanding of fundamental mathematical principles, analyzing complex mathematical processes, and applying theoretical frameworks to real-world scenarios.