Freelance Energy Engineer And Python Expert AI Trainer

Job description / Role

Description

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 rigorous energy engineering problems reflecting professional practice;
• Evaluate AI solutions for correctness, assumptions, and constraints;
• Validate calculations or simulations using Python (NumPy, Pandas, SciPy);
• Improve AI reasoning to align with industry-standard logic;
• Apply structured scoring criteria to multi-step problems.

What we look for

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

• Degree in Energy Engineering or related fields, e.g. Electrical Engineering, Power Systems Engineering, Renewable Energy Engineering, Electronics etc.
• 3+ years of professional energy engineering experience
• Strong written English (C1/C2)
• Strong Python proficiency for numerical validation
• Stable internet connection

Professional certifications (e.g., PE, CEng, EMP, CEM) and experience in international or applied projects are an advantage.

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.

Payment

Paid contributions, with rates up to $40/hour*

Fixed project rate or individual rates, depending on the project

Some projects include incentive payments

Note: Rates vary based on expertise, skills assessment, location, project needs, and other factors. Higher rates may be offered to highly specialized experts. Lower rates may apply during onboarding or non-core project phases. Payment details are shared per project.