Mathematics Professor/Researcher

Job Summary

The Mathematics Professor/Researcher reviews, evaluates, and refines complex mathematical datasets to guide AI model alignment. Core responsibilities include authoring graduate-level proofs, constructing robust problem sets, evaluating synthetic model outputs for logical soundness, writing step-by-step instructional guides, and collaborating with cross-functional AI data architects to establish rigorous quality benchmarks.

Key Responsibilities

1. Mathematical Content Evaluation & Feedback (35%)

• Evaluate AI Outputs: Review, cross-examine, and grade complex mathematical lines of reasoning generated by frontier AI models to ensure absolute logical soundness.
• Provide Expert Feedback: Draft highly detailed, domain-specific critiques highlighting hidden mathematical fallacies, subtle notation errors, or gaps in proof structures.
• Refine Data Quality: Annotate and clean mathematical datasets, correcting errors in formatting, step-by-step logic, and pedagogical value.

2. Problem Creation & Curriculum Development (35%)

• Author Advanced Proofs: Design and write original mathematical problems, detailed proofs, and companion instructional sets spanning a broad spectrum of undergraduate and graduate topics.
• Format Technical Content: Structure all mathematical problems, equations, and solutions using precise typography (e.g., standard LaTeX notation) to optimize model ingestion.
• Design Edge Cases: Construct challenging edge cases, trick questions, and multi-disciplinary math problems specifically tailored to test the boundaries of AI reasoning engines.

3. Collaboration, Documentation & Instruction (30%)

• Write Step-by-Step Solutions: Draft clear, transparent, and pedagogically sound solutions that map out the exact logical jumps required to solve complex problems.
• Define Quality Benchmarks: Collaborate with international cohorts of mathematicians and micro1 data architects to establish clear quality baselines and expand subject matter coverage.
• Participate in Data Reviews: Join iterative peer-review workshops aimed at boosting the structural diversity, depth, and reliability of micro1’s mathematics training modules.

Experience & Competencies

Candidates must possess an advanced academic pedigree and a clear passion for technical communication:

Academic & Research Background

• Minimum Requirement: A completed PhD in Mathematics or a closely matching quantitative discipline (e.g., Theoretical Physics or Core Statistics).
• Teaching or Research Tenure: Proven experience conducting advanced, independent mathematical research or delivering lectures within a higher education/university setting.
• Literary Currency: Deep familiarity with contemporary mathematical literature, foundational axioms, and ongoing modern research trends across disciplines.

Functional Skills & Execution Rigor

• Pedagogical Asset Design: Experience authoring original problem sets, textbook chapters, or reference guides designed for advanced mathematics curricula is highly desirable.
• Clear Communications Engine: Exceptional written and verbal English communication skills, with a proven ability to break down highly abstract spatial and relational structures into clear, unambiguous prose.
• Meticulous Detail Focus: Strong analytical tracking skills, with an innate ability to spot minor logical jumps, arithmetic slips, or notation errors across long strings of text.
• Technical Interest: A natural intellectual curiosity regarding artificial intelligence, large language model reasoning tracks, and the long-term impact of automated systems on scientific discovery.

Expected Outputs & Deliverables

• Original, verified mathematical problem sets and multi-step solutions formatted using clean Markdown and LaTeX syntax.
• Detailed quality evaluation scorecards, peer annotations, and logic-gap corrections for large-scale mathematical training blocks.
• Comprehensive technical instructional guides and step-by-step problem-solving frameworks designed for AI engine training.
• Weekly contributions to micro1's distributed data pipeline, backed by clear documentation tracking solved problems and reviewed datasets.

Skills Required:

• Education / Teaching / Training