Research Physicist with Python Experience – Freelance AI Trainer

Remote from: Romania
Salary, yearly, USD: 90,000
Department: Data Science & Analytics
Employment type: Full Time,
Job posted: 11 May 2026
Apply before: 11 Jun 2026
Views / Applies: 159 / 33

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 with libraries like NumPy and SciPy. Tasks include creating problems in mechanics, electromagnetism, thermodynamics, and quantum mechanics that simulate real research workflows. Candidates need a physics degree and 2+ years of experience, with strong problem-solving and programming skills. The project-based opportunity offers up to $45/hour equivalent, with an estimated 10-20 hours per week. This is ideal for physicists seeking part-time, non-permanent work in AI development.

Job Complexity

Easy Hard

AI Insight The role requires advanced physics knowledge, Python programming, and creative problem design, which is challenging but not the hardest due to the freelance nature and specific focus.

Salary Analysis

AI Insight The offered salary of $90,000 per year (based on $45/hour at 40 hours/week) is competitive for a research physicist role, though the freelance nature may vary. The market range for similar roles is $70,000-$130,000, so this is near the median.

Key Skills

Python NumPy SciPy Computational Physics Quantum Mechanics Thermodynamics Electromagnetism Numerical Simulation Problem Design AI Training

Cover Letter Sample

I am writing to express my interest in the Research Physicist with Python Experience position. With a PhD in Physics and over 3 years of experience in computational modeling, I have developed a strong ability to design challenging problems that mirror real research workflows. My proficiency in Python, NumPy, and SciPy enables me to create computationally intensive problems requiring non-trivial reasoning.

I am particularly excited about the opportunity to contribute to AI training by designing problems that push the boundaries of physics simulations. My background in thermodynamics and quantum mechanics allows me to craft diverse and engaging challenges. I am confident in my ability to deliver high-quality work and meet the project requirements.

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

Possible Interview Questions

Describe a complex computational physics problem you have designed or solved using Python.

I once designed a problem simulating the heat diffusion in a 2D plate with irregular boundaries, using finite difference methods in NumPy. The problem required solving the PDE numerically and analyzing convergence, which involved non-trivial reasoning about boundary conditions and stability.

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

I incorporate iterative numerical methods, large parameter sweeps, or complex geometries that require numerical integration or solving systems of equations. For example, using Monte Carlo simulations or solving coupled ODEs with adaptive timestepping ensures manual solution is impractical.

Explain your experience with Python libraries like SciPy and SymPy in the context of physics simulations.

I have used SciPy for optimization and integration, and SymPy for symbolic manipulation in deriving equations. For instance, I used SymPy to derive the equations of motion for a double pendulum and then simulated it with SciPy's ODE solver.

How would you design a problem that tests understanding of quantum mechanics while requiring Python programming?

I would design a problem to compute the energy eigenvalues of a particle in a 1D infinite potential well with a perturbation, using NumPy to solve the Schrödinger equation numerically. The problem would require students to implement the perturbation theory and compare with exact solutions.

What strategies do you use to document problem statements clearly and provide verified correct answers?

I write step-by-step explanations, include diagrams, and provide code snippets with comments. I verify solutions by running my own Python scripts and checking against analytical results where possible, and I include unit tests to ensure correctness.

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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