Mindrift
ElectricalEngineer&PythonExpert-FreelanceAITrainer
Neural analysis suggests this role is
optimal for Mid+ candidates.
“Electrical Engineer & Python Expert - Freelance AI Trainer at Mindrift. Skills: Electrical Engineering, Python, AI Training. Design computational engineering problems. Write Python reference solution”
What You'll Achieve.
Succeed in small number of attempts; Task scores within range; Ensure task quality is high; Pass rate in 10–30% band
Industry & Context.
Design problems; Tune problem difficulty; Debug simulation stalls; Debug solver convergence
What They're Looking For.
Must Have
Degree in Electrical Engineering, 2+ years research, applied, or teaching, Python proficiency, written English (C1+)
Nice to Have
Fluency with scriptable electrical engineering package, Willingness to learn scriptable electrical engineering package
What You'll Do.
Design computational engineering problems
Write Python reference solution
Supply circuit definitions
Decide numerical answer
Test problem against model
Tune problem difficulty
Rewrite circuit topologies
Tighten operating conditions
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 ngspice, scikit-rf, python-control, pandapower, 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 netlist files and circuit 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 circuit topologies, tightening operating 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 intuition for how a fron
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