Mathematics & Python Expert – Freelance AI Trainer

Remote from: Portugal
Salary, yearly, USD: 58,000
Department: Data Science & Analytics
Employment type: Full Time,
Job posted: 11 May 2026
Apply before: 11 Jun 2026
Experience level: Senior
Views / Applies: 432 / 51

Mindrift connects AI experts and clients to advance Generative AI models.

Actively Hiring

AI Summary

This freelance role involves designing computational mathematics problems for AI training, requiring Python proficiency and expertise in areas like number theory, combinatorics, and numerical analysis. Contributors work on project-based tasks, creating problems that mimic real research workflows and verifying solutions. Ideal candidates have a degree in mathematics, 2+ years of experience, and strong English skills. The position offers flexible, part-time work with compensation up to $29 per hour. It is suitable for mathematicians with programming skills seeking non-permanent, remote opportunities.

Job Complexity

Easy Hard

AI Insight The role requires a high level of expertise in both advanced mathematics and Python programming, including designing complex problems and verifying solutions, which is challenging for most professionals.

Salary Analysis

AI Insight The offered salary of up to $29/hour equates to roughly $58,000 per year at 20 hours/week, which is below the typical market median for a mathematics and Python expert role (around $75,000). However, considering the freelance, project-based nature, the rate is competitive for part-time, non-permanent work.

Key Skills

Mathematics Python NumPy SciPy SymPy Numerical Analysis Combinatorics Graph Theory AI Training Problem Design

Cover Letter Sample

I am writing to express my strong interest in the Mathematics & Python Expert - Freelance AI Trainer position. With a Master's degree in Pure Mathematics and over three years of experience applying Python to solve complex computational problems, I am confident in my ability to design challenging problems that push the boundaries of AI reasoning. My background includes extensive work with numerical methods, symbolic computation, and libraries such as NumPy, SciPy, and SymPy, which align perfectly with the requirements of this role.

In my previous role as a research assistant, I developed algorithms for graph theory applications and created problem sets for undergraduate courses, honing my ability to design problems that mirror real research workflows. I am particularly drawn to the opportunity to contribute to AI advancement while working flexibly on projects that leverage my mathematical expertise. I am eager to apply my skills to create problems that require non-trivial reasoning and computational intensity.

Thank you for considering my application. I look forward to the possibility of contributing to your projects and discussing how my background can support your goals.

Possible Interview Questions

Can you describe a complex mathematical problem you have designed that required Python programming to solve?

I once designed a problem involving the enumeration of non-isomorphic graphs with given degree sequences, which required using networkx and itertools to generate and filter graphs, and then implementing a backtracking algorithm to count them efficiently. The problem was computationally intensive and could not be solved manually.

How do you ensure that the problems you create are computationally intensive and cannot be solved manually within reasonable timeframes?

I focus on problems that involve large search spaces, iterative numerical methods, or symbolic manipulations that scale with input size. For example, I might create a problem requiring solving a system of polynomial equations with many variables, where manual solution would take weeks, but a Python script using sympy can find solutions in minutes.

What experience do you have with numerical methods and symbolic computation?

I have used SciPy for numerical integration and optimization, and SymPy for symbolic differentiation and solving equations. In a recent project, I implemented a symbolic computation to derive the gradient of a complex function, then used NumPy for numerical evaluation in an optimization loop.

How do you verify that your problem solutions are correct and that the problems are well-posed?

I write test cases with known small instances, verify solutions against brute-force or alternative methods, and check edge cases. I also ensure that the problem statement is unambiguous and that the correct answer is uniquely defined. For probabilistic problems, I might use Monte Carlo simulations to validate.

Can you give an example of a problem that mirrors real mathematical research workflows?

A problem could involve investigating the distribution of prime gaps using computational number theory. The task might be to compute the maximal gap between primes up to a certain limit, requiring implementation of a sieve algorithm and analysis of results, mimicking research in analytic number theory.

Please submit your CV in English and indicate your level of English proficiency.

Mindrift connects specialists with project-based AI opportunities for leading tech companies, focused on testing, evaluating, and improving AI systems. Participation is project-based, not permanent employment.

What this opportunity involves

While each project involves unique tasks, contributors may:

Design original computational mathematics problems that simulate real mathematical research workflows;
Create problems requiring Python programming to solve (using Numpy, SciPy, Sympy);
Ensure problems are computationally intensive and cannot be solved manually within reasonable timeframes (days/weeks);
Develop problems requiring non-trivial reasoning chains in areas like number theory, combinatorics, graph theory, and numerical analysis;
Base problems on real research challenges or practical applications from mathematical practice;
Verify solutions using Python with standard mathematical libraries;
Document problem statements clearly and provide verified correct answers.

What we look for

This opportunity is a good fit for mathematicians with an experience in python open to part-time, non-permanent projects. Ideally, contributors will have:

Degree in Mathematics (Pure or Applied) or related fields;
Python proficiency for numerical validation. MATLAB, R, C, SQL, Numpy, Pandas, SciPy, domain-specific libraries, Stata or knowledge of any programming language can be equivalent;
2+ years of professional experience: applied, research, or teaching experience is applicable;
Experience with numerical methods and symbolic computation;
Ability to design problems that mirror real mathematical research workflows;
Familiarity with computational complexity theory;
Strong written English (C1+).

How it works

Apply → Pass qualification(s) → Join a project → Complete tasks → Get paid

Project time expectations

For this project, tasks are estimated to require around 10–20 hours per week during active phases, based on project requirements. This is an estimate, not a guaranteed workload, and applies only while the project is active.

Compensation

On this project, contributors can earn up to $29 per hour equivalent, depending on their level and pace of contribution.

Compensation varies across projects depending on scope, complexity, and required expertise. Please note that other projects on the platform may offer different earning levels based on their requirements.

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