Physics & Python Expert - Freelance AI Trainer

Mindrift•Quebec, QC

1d•Remote

About The Position

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. 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. You write a Python reference solution, supply input files and domain or initial condition definitions where needed. You decide the numerical answer and how close the model needs to get — with a domain-appropriate tolerance — to count as right. You 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 also get a hands-on working intuition for how a frontier model navigates complex electromagnetic, fluid, gravitational, and cosmological problems.

Requirements

Degree in Physics (Theoretical, Experimental, or Computational) or related field
2+ years of research, applied, or teaching experience
Python proficiency for writing reference solutions
Fluency with — or strong 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, or bilby
Ability to design problems that genuinely require a specialized simulation tool
Strong written English (C1+)
Physicists with experience in Python
Open to part-time, non-permanent projects

Nice To Haves

No prior experience with the listed tools is acceptable if ready to get up to speed independently and hit the ground running.

Responsibilities

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.
Ensure task quality is high through feedback from a senior reviewer.