Physics Expert with Python – Freelance AI Trainer

Remote from: France
Salary, yearly, USD: 90,000
Department: Data Science & Analytics
Employment type: Full Time,
Job posted: 28 May 2026
Apply before: 28 Jun 2026
Experience level: Senior
Views / Applies: 20 / 7

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

Actively Hiring

AI Summary

This freelance role involves designing original computational physics problems for AI training, requiring Python proficiency (Numpy, SciPy, Sympy) and a physics degree. Tasks include creating problems that simulate real research workflows, ensuring computational intensity, and verifying solutions. The position is project-based with an estimated 10-20 hours per week, offering up to $45/hour. Ideal for physicists with 2+ years experience and strong written English.

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 requires advanced physics knowledge and Python skills to design complex computational problems, but as a freelance position with clear guidelines, it balances challenge with flexibility.

Salary Analysis

AI Insight The offered salary of up to $45/hour equates to roughly $93,600 annually for a 40-hour week, which is competitive for freelance physics roles. Market range for similar positions is $70k-$120k, placing this offer near the median.

Key Skills

Physics Python Computational Physics NumPy SciPy SymPy Numerical Simulation Problem Design AI Training Freelance

Cover Letter Sample

Dear Hiring Team,

I am writing to express my interest in the Physics Expert with Python - Freelance AI Trainer position. With a Master's degree in Physics and over 3 years of experience in computational research, I have developed strong skills in Python and numerical simulation libraries like NumPy and SciPy. My background includes designing complex physics problems for research and teaching, which aligns perfectly with your need for creative problem design.

I am particularly drawn to the opportunity to contribute to AI training by creating problems that mirror real physics workflows. My experience with mechanics, electromagnetism, and quantum mechanics ensures I can develop challenging and accurate problems. I am confident in my ability to verify solutions using Python and document them clearly.

Thank you for considering my application. I look forward to the possibility of collaborating on this innovative project.

Sincerely, [Your Name]

Possible Interview Questions

Can you describe a complex physics problem you designed that required Python to solve?

I designed a problem simulating the trajectory of a charged particle in a non-uniform magnetic field. Using SciPy's ODE solver, I modeled the motion and required numerical integration due to field variations. The problem tested understanding of Lorentz force and computational methods.

How do you ensure a physics problem is computationally intensive and cannot be solved manually?

I incorporate elements like nonlinear dynamics, coupled differential equations, or Monte Carlo simulations. For example, a problem involving chaotic pendulum motion with damping requires iterative numerical methods and cannot be solved analytically.

What experience do you have with numerical simulation methods in Python?

I have used finite difference methods for solving PDEs in heat transfer problems, and Monte Carlo simulations for statistical mechanics. I'm proficient with NumPy for array operations, SciPy for optimization and integration, and SymPy for symbolic manipulation.

How do you approach designing problems that mirror real research workflows?

I base problems on actual research scenarios, such as analyzing experimental data from particle accelerators or modeling astrophysical phenomena. I ensure the problem requires steps similar to real research, like data preprocessing, simulation, and verification.

Can you give an example of a problem you created that involves mechanics and electromagnetism?

I designed a problem about a charged particle in a magnetic mirror trap. The particle's motion involves both Lorentz force and conservation of magnetic moment. Solving requires numerical integration of equations of motion, combining mechanics and electromagnetism.

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 physics problems that simulate real physics 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 mechanics, electromagnetism, thermodynamics, and quantum mechanics;
Base problems on real research challenges or practical applications from physics practice;
Verify solutions using Python with standard physics simulation libraries;
Document problem statements clearly and provide verified correct answers.

What we look for

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

Degree in Physics (Theoretical, Experimental, or Computational) 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 simulation methods;
Ability to design problems that mirror real physics research workflows;
Creative thinking in problem design across diverse physics areas;
Familiarity with physics modeling and approximation techniques;
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 $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.

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