Mathematician with Python Proficiency - AI Trainer

Job Description

Mathematics Professional - AI Model Evaluation (Remote)

Location: Austin, TX

• Remote
  
  

About the Role: We're building a talent pool of Mathematics professionals with Python proficiency to contribute to project-based AI development initiatives, focused on evaluating and enhancing frontier AI models. Designed for mathematicians who enjoy deep technical problem-solving, this role involves applying analytical expertise to evaluate and push the boundaries of frontier AI models using domain-specific tools such as Z3, cvc5, SageMath, or Macaulay2, with verifiable, code-graded answers run inside isolated Linux environments.

Key Responsibilities

• Identify an appropriate mathematical software package and build problems whose solution genuinely hinges on that tool's core capabilities.
• Develop full Python solutions for each problem, providing input files where applicable.
• Establish the correct answer and define the acceptable margin for the AI model's response, based on the mathematical context.
• Run the problem against the AI model across multiple parallel attempts, analyzing where it succeeds or falls short, and adjusting difficulty until the pass rate falls between 10% and 30%.
• Rewrite and refine problem parameters iteratively, building an understanding of how the model navigates complex mathematical challenges.
• Hand off completed tasks to a senior reviewer in your subfield and refine based on their feedback before final submission.
  
  

Core Requirements

• Academic background in Mathematics, Pure or Applied, or an equivalent field.
• At least 2 years of hands-on experience in mathematics research, applied work, or teaching.
• Solid Python skills, applied to writing and validating computational solutions.
• Capacity to build problems that cannot be solved without specialized mathematical software.
• Excellent written and verbal communication skills in English.
• Ability to work independently in a remote, fast-paced environment.
  
  

Nice-to-Have

• Working knowledge of one or more domain-specific mathematical tools, including but not limited to Z3, cvc5, Macaulay2, Singular, CasADi, IPOPT, SDPB, G6K, fpylll, or SageMath, or a demonstrated ability to get up to speed independently.
• Prior exposure to how frontier AI models approach complex scientific problems.
• Knowledge spanning more than one area of mathematics, such as algebra, optimization, formal verification, or computational number theory.
• Familiarity with containerized or sandboxed Linux execution environments.
  
  

Note: Due to the high volume of applications, only shortlisted candidates will be contacted.

We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.