About the job
We invite you to submit your CV in English and specify your level of English proficiency.
Mindrift facilitates connections between specialists and project-oriented AI roles with top technology firms, dedicated to the assessment, evaluation, and enhancement of AI systems. This is a project-based opportunity, not a permanent position.
Opportunity Overview
Each project presents distinct tasks, which may include:
- Formulating rigorous physics problems that mirror professional practice;
- Assessing AI solutions for accuracy, assumptions, and limitations;
- Validating calculations or simulations through Python (utilizing libraries such as NumPy, Pandas, SciPy);
- Enhancing AI reasoning to conform to industry-standard logic;
- Employing structured scoring criteria for complex, multi-step problems.
Ideal Candidate
This role is well-suited for physicists with a strong Python background who are interested in part-time, temporary projects. The ideal candidates will possess:
- A degree in Physics or related disciplines, such as Engineering Physics, Thermodynamics, Statistical Mechanics, or Acoustics;
- A minimum of 3 years’ professional experience in physics;
- Proficient written English skills (C1/C2 level);
- Advanced Python skills for numerical validation;
- A reliable internet connection.
Professional certifications (e.g., CPhys, EurPhys, MInstP) and experience with international or applied projects are advantageous.
Process Overview
Application → Qualification Assessment → Project Engagement → Task Completion → Compensation
Project Commitment Expectations
For this initiative, tasks are estimated to require approximately 10-20 hours per week during active phases, subject to project needs. Note that this is an estimate and does not guarantee a specific workload.
Compensation
Contributors can earn up to $34 per hour, contingent upon their contribution level and speed. Compensation varies by project based on its scope, complexity, and required expertise. Other projects on the platform may offer different earning potentials based on their specific requirements.

