Quantum Research Scientist with Python – Freelance AI Trainer

Remote from: Romania
Salary, yearly, USD: 90,000
Department: Data Science & Analytics
Employment type: Full Time,
Job posted: 28 May 2026
Apply before: 28 Jun 2026
Views / Applies: 23 / 8

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

Actively Hiring

AI Summary

This is a freelance AI trainer role focused on designing computational physics problems for AI training. The work involves creating Python-based problems in quantum mechanics, electromagnetism, and thermodynamics that simulate real research workflows. Candidates need a physics degree and strong Python skills, with experience in numerical simulation. The role is project-based with an estimated 10-20 hours per week. Compensation is up to $45 per hour, equivalent to a yearly salary range based on typical hours.

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 expertise in quantum physics and Python, with the ability to design original, computationally intensive problems, making it highly challenging.

Salary Analysis

AI Insight The offered salary (up to $45/hour) translates to roughly $93,600 yearly for 40-hour weeks, which is competitive for a freelance quantum research scientist role. The market range for similar roles in the US is $80,000-$150,000, with median around $110,000.

Key Skills

Quantum Mechanics Python NumPy SciPy Computational Physics Numerical Simulation Problem Design Freelance AI Training Physics Research

Cover Letter Sample

I am writing to express my strong interest in the Quantum Research Scientist with Python - Freelance AI Trainer position. With a PhD in Physics and over 4 years of experience in computational quantum mechanics, I have developed expertise in designing numerical simulations using Python libraries like NumPy and SciPy. My background includes creating complex physics problems for research and teaching, which aligns perfectly with the requirement to design original computational problems for AI training.

I have a proven track record of developing computationally intensive problems that require non-trivial reasoning chains in quantum mechanics and thermodynamics. My proficiency in Python and numerical simulation methods ensures that I can verify solutions accurately and document problem statements clearly. I am comfortable working independently and can commit to the estimated 10-20 hours per week.

I am excited about the opportunity to contribute to cutting-edge AI systems and look forward to the possibility of joining your team. Thank you for considering my application.

Possible Interview Questions

Can you describe a complex physics problem you designed for AI training, including the reasoning chain and how you verified the solution?

I designed a problem on quantum tunneling through a potential barrier, requiring the candidate to implement a numerical solution using the transfer matrix method in Python. The problem involved multiple steps: setting up the potential, computing transmission coefficients, and analyzing energy dependence. I verified the solution by comparing results with analytical approximations and benchmarking against known test cases.

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

I design problems that require iterative numerical methods, such as solving differential equations or performing Monte Carlo simulations, which involve large parameter sweeps or high-dimensional integrals. For example, a problem on electron scattering in a crystal lattice requires diagonalizing a large Hamiltonian matrix, which is impractical by hand.

What experience do you have with Python libraries like NumPy, SciPy, and SymPy in the context of physics simulations?

I have used NumPy for array operations and linear algebra, SciPy for integration and optimization, and SymPy for symbolic manipulations. For instance, I used SciPy's solve_ivp to simulate particle trajectories in a magnetic field, and SymPy to derive analytical expressions for perturbation theory problems.

How do you stay current with advancements in quantum mechanics and computational methods?

I regularly read journals like Physical Review Letters and attend conferences such as APS March Meeting. I also follow online courses and participate in open-source projects related to quantum computing, such as Qiskit and Cirq.

Describe a time when you had to design a problem that required creative thinking across multiple physics areas.

I created a problem combining electromagnetism and thermodynamics: simulating the heating of a metallic nanoparticle under an oscillating electric field. The problem required solving Maxwell's equations for the field distribution and then using the heat equation to model temperature rise, integrating concepts from both fields.

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 quantum researchers 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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