CFD and Aerodynamics AI Training Reviewer

About OpenTrain

OpenTrain is the #1 platform for finding and building careers in AI training and data labeling. OpenTrain AI recruits contributors for specialized projects where human expertise helps improve the quality, reliability, and usefulness of modern AI systems.

Creating an OpenTrain account is free, and contributors can build a profile that reflects their technical experience and apply to relevant opportunities in minutes.

About AI Training Work

AI training is the human side of building artificial intelligence. For technical projects, subject-matter experts create, solve, validate, and evaluate challenging examples so AI systems can produce more accurate and physically sound results.

This work is fully remote and often flexible, making it possible to contribute part time while applying specialized engineering knowledge to cutting-edge AI development.

The Role

OpenTrain AI is recruiting a CFD and Aerodynamics AI Training Reviewer to create, solve, review, and validate computational fluid dynamics and aerodynamic engineering tasks for AI training. You will develop reproducible workflows, prepare solver-ready cases, automate simulations with Python, and assess whether engineering outputs are numerically sound and physically realistic.

This is a global, fully remote contractor opportunity with approximately 15 hours per week and flexible scheduling. The listed compensation range is $80 to $130 USD per hour; applicable compensation terms should be confirmed before work begins.

• Contractor and part-time opportunity
• Worldwide and fully remote
• Approximately 15 hours per week
• Flexible schedule
• Listed compensation range of $80 to $130 USD per hour

What You'll Do

You will formulate aerodynamic and fluid dynamics problems as computational models and produce reproducible reference solutions and verification criteria. Your work will combine engineering judgment, simulation execution, numerical analysis, and careful review of AI-generated solutions.

• Create or modify geometries, computational domains, and meshes.
• Define fluid properties, initial conditions, boundary conditions, and solver settings.
• Run CFD workflows through command-line tools, scripts, configuration files, or programmatic interfaces.
• Use Python for preprocessing, simulation execution, parameter sweeps, postprocessing, and validation.
• Analyze pressure, velocity, forces, moments, lift, drag, pressure loss, and related outputs.
• Diagnose mesh quality, convergence, numerical stability, turbulence modeling, boundary-condition, and physical-validity issues.
• Perform mesh-independence studies and compare results with analytical, experimental, or reference data.
• Review AI-generated engineering solutions and identify incorrect assumptions or configurations.
• Develop reproducible reference solutions and verification criteria for AI training tasks.

Required Skills and Qualifications

Strong fluid mechanics and applied aerodynamics knowledge is required, along with practical experience conducting CFD or aerodynamic analyses. You should be able to explain and defend the technical choices behind your models, simulations, and evaluations.

An MS or PhD in Aerospace Engineering, or Mechanical Engineering with an aerodynamics, fluid dynamics, or thermofluids specialization, is relevant. A BS in Aerospace or Mechanical Engineering with one to two years of aerodynamics-focused professional or research experience is also applicable.

• Advanced aerospace or mechanical engineering knowledge focused on aerodynamics, fluid dynamics, or thermofluids
• Experience formulating and executing CFD or aerodynamic analyses
• Practical Python and scientific computing experience
• Python automation for preprocessing, solver execution, parameter sweeps, and postprocessing
• Ability to justify modeling assumptions, boundary conditions, mesh strategies, and solver settings
• Ability to diagnose mesh, convergence, boundary-condition, turbulence-modeling, and physical-validity problems
• Experience with at least one command-line-accessible or programmatically configurable engineering tool

Relevant Tools and Technical Experience

Experience with the tools below may be useful. Equivalent programmatic engineering tools are also relevant when they support the required simulation, automation, analysis, or validation workflows.

• OpenFOAM, SU2, Gmsh, FEniCSx, Elmer, or CalculiX
• SciPy, NumPy, pandas, Matplotlib, or Jupyter
• Solver APIs and mesh-processing libraries
• Command-line-accessible or programmatically configurable engineering software

Why This Work Matters

Every major AI system depends on examples prepared and reviewed by people. In this role, your expertise helps teach AI to reason about computational models, numerical results, engineering assumptions, and the physical behavior of fluid systems.

OpenTrain gives contributors a place to grow an AI training portfolio, discover work aligned with their skills, and develop experience at the intersection of engineering and artificial intelligence.

How to Apply

Create a free OpenTrain account, build your profile around your CFD, aerodynamics, Python, and scientific computing experience, and apply through OpenTrain. Be prepared to demonstrate how you formulate, execute, validate, and review aerodynamic or fluid dynamics analyses.