Computational Engineering AI Evaluator

About OpenTrain

OpenTrain is the centralized platform where people launch and grow careers in AI training and data labeling. The platform helps freelancers discover projects, consolidate opportunities, and build a unified portfolio of AI training work they control.

OpenTrain focuses on the human side of building AI systems: contributors perform the detailed review, annotation, and evaluation tasks that make modern models reliable and useful.

About AI training work in engineering

AI training (also called data labeling or evaluation) is how humans teach models to perform technical tasks. In computational engineering this means reviewing model outputs against realistic simulations, improving datasets, and helping build benchmarks so AI systems can reason about fluids, structures, controls, and multi-physics systems.

• This work is often remote and flexible, and it puts you at the cutting edge of how engineering AIs are built and validated.

The role

We are recruiting a Computational Engineering AI Evaluator to support benchmarking and evaluation across computational engineering domains such as CFD, FEA, robotics, control systems, and systems engineering. This is a contractor, part-time role requiring 20+ hours per week and is open to remote contributors worldwide.

OpenTrain lists the experience level as Entry level, but this role explicitly requires a PhD or equivalent industry or research experience in computational, simulation, or systems engineering.

• Employment: Contractor, Part-time
• Time: 20+ hours/week
• Language: English
• Location: Remote / Worldwide

What you'll do

• Evaluate AI model performance in realistic computational engineering scenarios and identify failure modes.
• Review and improve technical datasets, simulation workflows, and results for quality, fidelity, and accuracy.
• Validate engineering solutions using simulation and analysis tools such as ANSYS, Abaqus, COMSOL, OpenFOAM, MATLAB, and Python.
• Write detailed technical assessments and communicate findings clearly to interdisciplinary teams.
• Collaborate with domain experts to identify gaps and opportunities for model and dataset improvement.
• Help design and build robust benchmarks for AI systems in computational engineering fields.

Required qualifications

• PhD or equivalent industry or research experience in computational, simulation, or systems engineering (explicitly required).
• Proven expertise in CFD, FEA, computational mechanics, robotics, control systems, or signal processing.
• Hands-on experience with simulation, analytical, or technical modeling tools such as ANSYS, Abaqus, MATLAB, OpenFOAM, or similar.
• Comfort scripting in Python and/or MATLAB to analyze results and automate validation steps.
• Strong problem-solving and systems-thinking skills; ability to synthesize complex technical knowledge into actionable insights.
• Excellent written and verbal communication skills for technical documentation and cross-team collaboration.

Helpful background

• Experience applying AI or ML techniques within engineering workflows (model-in-the-loop, surrogate modeling, physics-informed ML).
• Familiarity with HPC, optimization, large-scale simulation environments, or parallel workflows.
• Publication record, patents, or deep R&D involvement in computational engineering or simulation is a plus.

Compensation, logistics & tools

Compensation is hourly with a listed rate up to $60/hour and an hourly range indicated as $20–$60/hr. This is a contractor engagement paid per hour.

Work centers on document-style evaluation and technical review (data type: Document). Labeling tasks include evaluation/rating and data-collection style activities, and the project uses Other labeling software (platform/tooling will be specified during onboarding).

• Label types: Evaluation/Rating, Data Collection
• Data type: Document
• Typical tools referenced: ANSYS, Abaqus, COMSOL, OpenFOAM, MATLAB, Python, scientific workflows
• Hours: 20+ hours/week, remote, worldwide

How to apply

Create or sign in to your OpenTrain account (creating an account is free) and submit an application for this project. Include your CV or resume and links to relevant publications, patents, code repositories, or sample technical reports that demonstrate your simulation and evaluation experience.

If you meet the required qualifications you may be invited to a technical screening and sample evaluation task to demonstrate your approach to benchmarking and documenting results.