Mindrift
PhysicsExpertwithPythonExpertise-FreelanceAITrainer
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“Physics Expert with Python Expertise - Freelance AI Trainer at Mindrift. Skills: Physics expertise, Python programming, Specialized physics package usage. Design computational physics problems to challenge a frontier AI model. Ensure problems have answers verifiable by code and require specialized tools”
What You'll Achieve.
Design tasks that challenge AI models; Achieve a specific AI agent success rate (10-30%); Ensure high task quality through review and feedback
Industry & Context.
Designing challenging physics problems; Tuning problem difficulty; Analyzing simulation issues; Understanding how AI agents cut corners
Project-based, not permanent employment, Work within a sealed Linux container, Willingness to independently learn tools if not already fluent
What They're Looking For.
Must Have
Degree in Physics (Theoretical, Experimental, or Computational) or related, 2+ years of research, applied, or teaching, Python proficiency for writing reference, Ability to design problems that genuinely require a specialized simulation, written English (C1+)
Nice to Have
Fluency with — or willingness to independently learn — at least one scriptable physics package: FEniCS / DOLFINx, OpenFOAM, Meep, MPB, openEMS, Geant4, PYTHIA8, ROOT / PyROOT, WarpX, REBOUND, MESA, CAMB, CLASS
What You'll Do.
Design computational physics problems to challenge a frontier AI model
Ensure problems have answers verifiable by code and require specialized tools
Write Python reference solutions
Supply input files and domain/initial condition definitions
Decide numerical answers and appropriate tolerances
Test problems against AI models in batches
Tune problem difficulty for a specific success rate (10-30%)
Rewrite field configurations
and solver parameters
Analyze AI agent behavior and simulation issues
How You'll Work.
Team & Collaboration
Tasks reviewed by senior reviewers in the subfield; Provide feedback to ensure task quality is high
Communication Scope
English proficiency (C1+)
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 is project-based, not permanent employment.** **What this opportunity involves** You design computational physics 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 FEniCS, OpenFOAM, Meep, REBOUND, CAMB, 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 physics models, integrators, Monte Carlo kernels, or PDE discretisations. • Write a Python reference solution, supply input files and domain or initial condition 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 field configurations, tightening initial conditions and solver parameters, and watching how the agents act. You'll learn how these agents cut corners, where a simulation stalls, where an integrator converges. This time compounds in two directions. You come out of each task with deeper command of the anchor tool itself, and a
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