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 graduate- and industry-level automotive engineering problems grounded in real practice; Evaluate AI-generated solutions for correctness, assumptions, and engineering logic; Validate analytical or numerical results using Python (NumPy, SciPy, Pandas); Improve AI reasoning to align with first principles and accepted engineering standards; Apply structured scoring criteria to assess multi-step problem solving.
What we look for This opportunity is a good fit for automotive engineers with an experience in python open to part-time, non-permanent projects.
Ideally, contributors will have:  Degree in Automotive Engineering or related fields, e.
g. Mechatronics, Manufacturing Engineering, Mechanical Engineering, Aerospace Engineering, etc.
3+ years of professional automotive engineering experience Strong written English (C1/C2) Strong Python proficiency for numerical validation Stable internet connection  Professional certifications (e.
g., PE, CEng, PMP) 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.
Compensation On this project, contributors can earn up to  $40 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.