Math Expert (US & International)
About the role
The future of AI — whether in training or evaluation, classical ML or agentic workflows — starts with high-quality data.
At HumanSignal, we’re building the platform that powers the creation, curation, and evaluation of that data. From fine-tuning foundation models to validating agent behaviors in production, our tools are used by leading AI teams to ensure models are grounded in real-world signal, not noise.
Our open-source product, Label Studio, has become the de facto standard for labeling and evaluating data across modalities — from text and images to time series and agents-in-environments. With over 250,000 users and hundreds of millions of labeled samples, it’s the most widely adopted OSS solution for teams working on building AI systems.
Label Studio Enterprise builds on that traction with the security, collaboration, and scalability features needed to support mission-critical AI pipelines — powering everything from model training datasets to eval test sets to continuous feedback loops.We started before foundation models were mainstream, and we’re doubling down now that AI is eating the world. If you're excited to help leading AI teams build smarter, more accurate systems — we’d love to talk.
About the Role
HumanSignal is seeking highly qualified mathematics experts to contribute to cutting-edge AI development projects. This role involves evaluating, annotating, and validating mathematical content to improve AI model performance in mathematical reasoning and problem-solving.
Compensation
Up to $110 USD/hour based on qualifications and project complexity
Required Qualifications
Education
Master's degree required in a mathematics-related field
PhD strongly preferred in:
Pure Mathematics
Applied Mathematics
Mathematical Statistics
Computational Mathematics
Mathematical Physics
Operations Research
Or closely related quantitative disciplines
Degree Field Requirements
Candidates must hold degrees directly in mathematics or mathematically-intensive fields. Acceptable programs include:
Mathematics (Pure/Applied)
Statistics/Mathematical Statistics
Mathematical Physics
Actuarial Science (with strong mathematical foundation)
Operations Research
Computational Mathematics
Responsibilities
Review and evaluate mathematical solutions for accuracy, rigor, and clarity
Annotate mathematical reasoning steps and problem-solving approaches
Validate AI-generated mathematical content across various difficulty levels
Identify errors in mathematical logic, notation, and methodology
Provide detailed feedback on mathematical explanations and proofs
Work with problems spanning algebra, calculus, statistics, linear algebra, differential equations, and advanced mathematics
Qualifications
Deep understanding of mathematical principles across multiple domains
Strong analytical and critical thinking skills
Excellent attention to detail and precision
Ability to clearly articulate mathematical reasoning
Experience with mathematical notation and LaTeX preferred
Familiarity with various problem-solving methodologies
Ability to work independently and meet deadlines
Work Details
Flexible, project-based work - work on your own schedule
Remote position - work from anywhere
Variable hours based on project availability and your capacity
Ongoing training and calibration provided
Quality-focused environment with performance feedback
Ideal Candidate Profile
Active researchers, professors, or recent graduates in mathematical fields
Strong publication record or demonstrated mathematical expertise
Experience in mathematical education, research, or industry applications
Passion for advancing AI capabilities in mathematical domains
How to Apply
Interested candidates should be prepared to:
Submit CV/resume highlighting mathematical qualifications
Provide proof of advanced degree(s)
Complete a brief mathematical assessment
Participate in a qualification review
HumanSignal is a leading provider of AI data annotation and labeling services, powering the development of next-generation AI systems. We work with top technology companies and research institutions to create high-quality training data.
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