Mathematics & Python Expert – Freelance AI Trainer

Remote from
Germany flag
Germany
Salary, yearly, USD
90,000
Employment type
Full Time,
Job posted
Apply before
8 Jul 2026
Experience level
Senior
Views / Applies
19 / 3

About Mindrift

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

Actively Hiring
Verified job posting
This job post has been manually reviewed for authenticity and compliance.

AI Summary

This freelance role involves designing advanced mathematics problems to challenge and evaluate frontier AI models. You will create problems requiring specialized tools like Z3, SageMath, or Macaulay2, and write Python reference solutions. The goal is to calibrate problem difficulty so that the AI succeeds only 10-30% of the time. The position offers flexibility with 10-20 hours per week during active project phases. Ideal candidates have a degree in mathematics, Python proficiency, and experience with scriptable mathematical packages.

Role DNA

Job Complexity
Easy Hard
Pace & Pressure
Relaxed Fast-paced
Autonomy Level
Guided Full Ownership
Communication Load
Independent Highly Collaborative
AI Insight The role demands deep expertise in mathematics and specialized tools, along with iterative problem tuning against an AI model, making it highly challenging.

Salary Analysis

Median Market Rate
USD90,000
US Market
USD70k – 130k
0 USD143k
AI Insight The offered salary of up to $45 per hour (equivalent to $90,000 annually for full-time work) is competitive for freelance AI training roles. However, market rates for such specialized expertise range from $70,000 to $130,000, so the position sits slightly below the midpoint.

Key Skills

Mathematics Python Z3 SageMath Macaulay2 AI Training Problem Design Algorithmic Thinking Formal Verification Scripting

I am excited to apply for the Mathematics & Python Expert role. With a degree in Mathematics and over 3 years of experience applying Python and specialized solvers like Z3 in research, I am confident in my ability to design challenging problems that push AI models to their limits.

My background includes developing numerical solutions for complex systems and teaching advanced math concepts, which aligns well with the requirement to calibrate problem difficulty. I am eager to apply my skills to improve frontier AI systems while deepening my own expertise in these tools.

I am particularly drawn to the iterative nature of the work and the opportunity to combine mathematical creativity with rigorous coding. I look forward to contributing to your projects.

Can you describe your experience with one of the specialized mathematical tools mentioned, such as Z3 or SageMath?
I have used Z3 extensively for solving constraint satisfaction problems in my research on program verification. I have written scripts to encode logical formulas and used its API to iterate over solutions, which has given me deep insight into its capabilities and limitations.
How would you design a problem that requires the use of a specialized solver and cannot be solved with NumPy or SymPy?
I would focus on problems involving non-linear constraints over integers, or problems that require symbolic reasoning like theorem proving. For example, a problem involving the satisfiability of a set of polynomial equations over a finite field would require a tool like Macaulay2, as NumPy cannot handle symbolic algebra.
What is your approach to calibrating problem difficulty to achieve a 10-30% pass rate for an AI model?
I would start by creating a baseline problem and testing it with the AI in parallel batches. Based on the pass rate, I would tighten constraints, add more steps, or require more specialized tool usage. I would also analyze where the AI fails and adjust the problem to ensure the solution is non-trivial but solvable with the right approach.
How do you ensure a problem's answer is verifiable by code?
I define the answer as a numerical value or a specific output from the tool. For example, I might ask for the number of solutions to a Diophantine equation, which can be computed exactly. I also provide a tolerance where needed, and I write a Python judge that compares the model's output to the reference solution.
Can you walk us through your process for troubleshooting when an AI model passes a problem too easily or fails too often?
If the model passes too easily, I analyze the problem to identify shortcuts and add additional constraints or steps that force deeper reasoning. If it fails too often, I simplify the problem by breaking it into smaller subtasks or providing more explicit hints in the problem statement. I iterate by testing batches until the pass rate falls in the desired range.

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

You design mathematics problems to challenge a frontier AI model. The problem must have an answer verifiable by code, and the problem has to require a specialized tool like Z3, cvc5, SageMath, Macaulay2, or others. NumPy or SymPy on their own won’t cut it. Each problem runs inside a sealed Linux container with the tool pre-installed and a programmatic judge that grades the model’s answer.
As an expert author, you:
• Pick an anchor tool and design a problem that hinges on its usage.
• Write a Python reference solution, supply input files optionally where needed.
• Decide the numerical answer and how close the model needs to get to count as right.
• Test the problem against the model in batches of parallel attempts, tuning the problem difficulty until the agent only succeeds in a small number of attempts.
• Once you’re happy with the task, and it scores within range, the task goes to a senior reviewer in your subfield. They will provide feedback to ensure task quality is high.
Calibration requires patience. You’re tuning the problem against batches of parallel runs of the agent, aiming for a pass rate in the 10-30% band. Reaching that means rewriting, re-tightening, and watching how the agents act. You’ll learn how these agents cut corners, where it stalls, where it converges. This time compounds in two directions. You come out of each task with deeper command of the anchor tool itself, and also get a hands on working intuition for how a frontier model navigates complex scientific problems.

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 field;
• 2+ years of research, applied, or teaching experience;
• Python proficiency for writing reference solutions; • Fluency with — or strong willingness to independently learn — at least one scriptable mathematical package: Z3, cvc5, Macaulay2, Singular, CasADi, IPOPT, SDPB, G6K, fpylll, or SageMath;
• Ability to design problems that genuinely require a specialized solver;
• Strong written English (C1+).
No prior experience with the listed tools? You’re still welcome to apply — as long as you’re ready to get up to speed on your own and hit the ground running.

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 $45 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.

Apply now >

This job listing has been manually reviewed by the Jobicy Trust & Safety Team for compliance with our posting guidelines, including verification of the company's legitimacy, accuracy of job details, clarity of remote work policy, and absence of misleading or fraudulent content.

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