About the job
We invite you to submit your CV in English, along with a clear indication of your English proficiency level.
Mindrift bridges the gap between skilled professionals and project-based AI opportunities with leading technology companies, concentrating on the testing, evaluation, and enhancement of AI systems. This role is project-based and does not entail permanent employment.
Key Responsibilities:
- Formulate complex energy engineering problems that reflect real-world applications;
- Assess AI solutions for accuracy, assumptions, and limitations;
- Validate calculations or simulations using programming languages such as Python (NumPy, Pandas, SciPy);
- Enhance AI reasoning to correspond with industry-standard logic;
- Utilize structured scoring criteria for multi-step problem-solving.
Qualifications:
This role is ideal for energy engineers with Python experience who are open to part-time, non-permanent projects. Preferred candidates will possess:
- A degree in Energy Engineering or a related field, such as Electrical Engineering, Power Systems Engineering, Renewable Energy Engineering, or Electronics;
- A minimum of 3 years of professional experience in energy engineering;
- Excellent written English skills (C1/C2 level);
- Proficient Python skills for numerical validation;
- A reliable internet connection.
Professional certifications (e.g., PE, CEng, EMP, CEM) and experience in international or applied projects will be considered advantageous.
Workflow:
Apply → Complete qualifications → Join a project → Fulfill tasks → Receive payment
Project Commitment:
Task completion is anticipated to take approximately 10–20 hours per week during active project phases, based on specific project requirements. This is an estimate and not a guaranteed workload.
Compensation:
- Compensated contributions, with rates up to $12/hour*
- Fixed project rates or individual rates, contingent on project specifics;
- Some projects may include performance-based incentives.
*Note: Compensation rates vary based on expertise, skills assessment, location, project requirements, and other factors. Highly specialized experts may receive higher rates, while lower rates may apply during onboarding or non-core project phases. Payment specifics will be communicated per project.

