Mindrift logo

Freelance AI Trainer: Physics Specialist with Python Expertise

MindriftRemote — València, Valencian Community, Spain
Remote Part-time $29/hr - $29/hr

Clicking Apply Now takes you to AutoApply where you can tailor your resume and apply.


Experience Level

Experience

Qualifications

A degree in Physics (Theoretical, Experimental, or Computational) or a related field. Proficiency in Python, with experience in numerical validation; knowledge of MATLAB, R, C, SQL, Numpy, Pandas, or SciPy is considered equivalent. A minimum of 2 years of professional experience in applied work, research, or academic teaching. Experience with numerical simulation techniques. Ability to conceptualize and design problems that reflect genuine physics research workflows. Innovative thinking in problem creation across various physics disciplines. Familiarity with modeling and approximation techniques in physics. Strong command of written English (C1+ level).

About the job

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

Mindrift is an innovative platform that connects talented specialists with project-based AI opportunities for prominent tech companies, emphasizing the testing, evaluation, and enhancement of AI systems. This is a project-based role, not a permanent position.

Opportunity Overview

Each project presents unique challenges, and contributors may be involved in the following tasks:

  • Crafting original computational physics problems that mirror authentic physics research workflows;
  • Developing problems that necessitate Python programming solutions (using libraries such as Numpy, SciPy, Sympy);
  • Ensuring problems are computationally demanding and cannot be resolved manually within reasonable timeframes (days/weeks);
  • Creating problems that require intricate reasoning chains in areas such as mechanics, electromagnetism, thermodynamics, and quantum mechanics;
  • Grounding problems in real-world research challenges or practical applications from the field of physics;
  • Validating solutions using Python alongside standard physics simulation libraries;
  • Clearly documenting problem statements and providing verified correct answers.

Ideal Candidate Profile

This role is well-suited for physicists with Python expertise seeking part-time, project-based work. The ideal candidate will possess:

  • A degree in Physics (Theoretical, Experimental, or Computational) or related fields;
  • Proficiency in Python for numerical validation; familiarity with MATLAB, R, C, SQL, Numpy, Pandas, SciPy, or any programming language is acceptable;
  • At least 2 years of professional experience in applied physics, research, or teaching;
  • Experience with numerical simulation methods;
  • The ability to design problems that replicate real physics research workflows;
  • Creative problem-solving skills across various physics domains;
  • Knowledge of physics modeling and approximation techniques;
  • Strong written English skills (C1 or higher).

Application Process

To apply, follow these steps: Submit your application → Pass the qualification assessments → Join a project → Complete assigned tasks → Receive payment.

Project Commitment

During active phases of projects, tasks are expected to require around 10–20 hours per week, depending on the specific project requirements. Note that this is an estimate and not a guaranteed workload.

Compensation

Contributors can earn up to $29 per hour, contingent upon their level of expertise and contribution speed. Compensation may vary based on project scope, complexity, and required skills. Please be aware that other projects may offer different compensation rates depending on their specific needs.

About Mindrift

Mindrift connects skilled specialists with project-based AI opportunities at leading tech companies, focusing on the evaluation and enhancement of AI systems.

Similar jobs

Browse all companies, explore by city & role, or SEO search pages. View directory listings: all jobs, search results, location & role pages.

Tailoring 0 resumes

We'll move completed jobs to Ready to Apply automatically.