Remote Freelance CFD AI Trainer—Fluid Simulation Expert

Mindrift; San Antonio (TX); À distance; USD 78 000 - 112 000; Temps partiel

Avantages offerts par ce poste

Remote freelance work

Flexible schedule

Performance bonus

Résumé du poste

Mindrift is seeking a skilled CFD specialist to train and evaluate AI agents on expert fluid simulation tasks. You will work with CAD geometry, set up and run simulations, and develop verified reference solutions while writing Python checks to validate the agent’s results.

Required experience includes CFD with common tools and strong verification practices, plus Python proficiency. This is a remote, project-based role offering flexible hours and performance-based rewards.

Qualifications

• Degree in Mechanical or Aerospace Engineering, or a related field; PhD preferred but not required.
• Several years of hands-on industry or research CFD experience with OpenFOAM, ANSYS Fluent/CFX or STAR-CCM+, including meshing.
• Solid verification and validation practice: mesh-independence studies, and validation against experiment or benchmarks.
• Comfortable with Python and GitHub.
• English at B2 level or above.
• Nice to have: aerospace, automotive, offshore or turbomachinery background; multiphase or particle-laden flow experience.

Responsabilités

• Train and evaluate AI agents on expert-level fluid simulation tasks using CFD tools.
• Take CAD geometry, set up simulations, run analyses, and build verified reference solutions.
• Write Python checks for the agent's results to ensure accuracy and reproducibility.

Description du poste

Mindrift is seeking a skilled CFD specialist to train and evaluate AI agents on expert fluid simulation tasks. You will work with CAD geometry, set up and run simulations, and develop verified reference solutions while writing Python checks to validate the agent’s results.

Required experience includes CFD with common tools and strong verification practices, plus Python proficiency. This is a remote, project-based role offering flexible hours and performance-based rewards.