AI Content Specialist (Mathematics & Python)
At a glance
Mid-level Content Creation role at Gramian Consulting Group. Remote, Pakistan · contract.
Pay not stated Content Creation salary
Growth Roles summary, based on the employer's posting.
What you'll do
- Construct mathematical coding tasks with one core problem and at least three linked sub-problems
- Build verified Python reference answers with full unit-test coverage and deterministic results
- Create tests that separate correct AI outputs from incorrect ones across multiple LLM judges
- Run CTP structure and quality checks, then revise tasks from review feedback
- Join review meetings and standups during the required overlapping work hours
What you bring
- A mathematics master's degree or PhD is required for this assignment
- Strong Python and scientific-computing ability, including tools such as NumPy, SciPy, or SymPy
- Experience turning rigorous mathematical specifications into tested, deterministic solutions
- Background in AI annotation, scientific research, or scientific writing, plus STEM research or project work
Who this fits
You’ll suit this four-week contractor assignment if you combine advanced mathematics with scientific Python and careful quality evaluation. The work is remote from Pakistan and allows up to forty hours weekly, with participation during required overlap hours. Familiarity with LLM evaluation, coding benchmarks, or assessment of AI-generated code also fits the role.
From the employer
Gramian Consultancy is a boutique consultancy specializing in IT professional services and engineering talent solutions. With a strong background in software engineering and leadership, we help companies build high-performing teams by matching them with professionals who truly fit their needs.
Role Overview
We are seeking a mathematics-focused professional to support an advanced AI research initiative developing high-quality STEM coding datasets. In this role, you will create rigorous mathematical problems, implement verified Python solutions, and develop evaluation tests designed to assess the capabilities of frontier AI models. The position combines mathematical problem-solving, scientific programming, and AI quality evaluation, with a strong emphasis on accuracy, determinism, and first-submission quality.
CONTRACT: Contractor assignment, 4 weeks
COMMITMENT: Hourly, up to 40h/week
LOCATIONS: Remote - Bangladesh, Brazil, Colombia, Egypt, Ghana, India, Indonesia, Pakistan, Turkey, Vietnam
PROCESS: Interest Check Form, CV and Google Scholar review
Key Responsibilities
- Author mathematical coding tasks containing one main problem and at least 3 logically connected sub-problems that progressively build toward the main solution.
- Implement verified golden solutions in Python with complete unit test coverage.
- Design discriminative test cases that distinguish correct from incorrect AI-generated outputs.
- Perform quality control validation using the Central Task Platform (CTP), including Tier 1 structure checks and Tier 2 quality rubrics.
- Refine problem specifications, solutions, and test cases based on QC feedback.
- Optimize tasks to meet Pass@K evaluation criteria across multiple LLM judges, including GPT, Gemini, and Nemotron.
- Ensure mathematical correctness, well-posedness, determinism, and compliance with project quality standards.
- Maintain high first-submission quality and minimize rework, targeting consistent L1 approval.
- Participate in review sessions, feedback meetings, and project standups during required overlap hours.
Requirements
- Master's degree or PhD in Mathematics.
- Strong Python programming skills with experience in scientific computing.
- Experience using scientific Python libraries such as NumPy, SciPy, SymPy, or relevant domain-specific tools.
- Ability to formulate rigorous mathematical problems with clear constraints and expected outputs.
- Experience implementing scientific or mathematical solutions with unit tests and deterministic outputs.
- Prior experience in AI data annotation, scientific research, or scientific writing.
- Familiarity with LLM evaluation frameworks, coding benchmarks, or AI-generated code assessment.
- Published research or academic project experience in mathematics or another STEM discipline.
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