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 a Python reference solution”
What You'll Achieve.
Task scores within range; Task quality is high; Pass rate in the 10–30% band
Industry & Context.
Design problems; Tune problem difficulty; Troubleshoot simulation stalls; Troubleshoot solver convergence
What They're Looking For.
Must Have
2+ years of research, applied, or teaching, Python proficiency for writing reference, Ability to design problems that genuinely require a specialized tool, written English (C1+)
Nice to Have
Degree in Mechanical Engineering or related, Fluency with — or willingness to independently learn — at least one scriptable mechanical engineering package: Cantera, CoolProp, CalculiX, OpenFAST, YADE, or similar tools from the broader engineering
What You'll Do.
Design computational engineering problems
Write a Python reference solution
Define numerical answer
Test the problem against the model
Tune problem difficulty
Rewrite thermodynamic cycles
Tighten material models
How You'll Work.
Team & Collaboration
Senior reviewer feedback
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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