Mindrift
MechanicalEngineer&PythonExpert-FreelanceAITrainer
Neural analysis suggests this role is
optimal for Mid+ candidates.
“Mechanical Engineer & Python Expert - Freelance AI Trainer at Mindrift. Skills: Mechanical Engineering, Python, AI Training. Design computational engineering problems. Write Python reference solution”
What You'll Achieve.
Succeeds in small number of attempts; Task scores within range; Task quality is high; Pass rate in 10–30% band
Industry & Context.
Design problems; Tune problem difficulty; Troubleshooting simulation stalls; Troubleshooting solver convergence
What They're Looking For.
Must Have
2+ years of research, Python proficiency, Degree in Mechanical Engineering, written English (C1+)
Nice to Have
Fluency with scriptable mechanical engineering package, willingness to independently learn mechanical engineering package, Ability to design problems requiring specialized tool
What You'll Do.
Design computational engineering problems
Write Python reference solution
Supply geometry definitions
Supply mechanism definitions
Decide numerical answer
Tune problem difficulty
Rewrite thermodynamic cycles
Tighten material models
Tighten boundary conditions
How You'll Work.
Communication Scope
English proficiency
Full Job 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 isproject-based, not permanent employment.** **What this opportunity involves** You design computational engineering problems to challenge a frontier AI model. The problem must have an answer verifiable by code, and the problem has to require a specialized tool like Cantera, CoolProp, CalculiX, OpenFAST, or others. Generic numerical libraries on their own won't cut it. Each problem runs inside a sealed Linux container with the tool pre-installed and a programmatic judge that grades the model's answer. As an expert author, you: • Pick an anchor tool and design a problem that hinges on its solvers, simulation kernels, or domain-specific models. • Write a Python reference solution, supply input files and geometry or mechanism definitions where needed. • Decide the numerical answer and how close the model needs to get — with a domain-appropriate tolerance — to count as right. • Test the problem against the model in batches of parallel attempts, tuning the problem difficulty until the agent only succeeds in a small number of attempts. • Once you're happy with the task, and it scores within range, the task goes to a senior reviewer in your subfield. They will provide feedback to ensure task quality is high. Calibration requires patience. You're tuning the problem against batches of parallel runs of the agent, aiming for a pass rate in the 10–30% band. Reaching that means rewriting thermodynamic cycles, tightening material models and boundary conditions, and watching how the agents act. You'll learn how these agents cut corners, where a simulation stalls, where a solver converges. This time compounds in two directions. You come out of each task with deeper command of the anchor tool itself, and also get a hands-on working
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