Mechanical Engineering AI Trainer (Python Required, BSc+1yr or MSc/PhD)

About OpenTrain

OpenTrain aggregates data-labeling and AI-training opportunities from many companies and platforms into one place so specialists can find work without searching dozens of sites. Creating an OpenTrain account is free and applying takes only a few minutes.

We list roles across the human side of AI development — labeling, evaluation, RLHF, data collection, and engineering-focused annotation — and connect qualified contractors with short- and long-term projects.

About AI training work

AI training (data labeling and human feedback) supplies the examples and judgments that modern models learn from. Contributors write and rate model outputs, evaluate technical solutions, annotate content, or produce verified answers that teach models to be accurate, safe, and useful.

This project focuses on mechanical engineering and simulation problems: your technical work will directly shape how models handle engineering reasoning, numerical workflows, and code-based solutions.

The role

You will train and evaluate advanced AI models by creating computationally intensive mechanical engineering problems and providing verified Python-based solutions. Tasks may require multi-step calculations and simulations using numerical methods and iteration.

This is a part-time contractor role (less than 20 hours/week), paid per hour. Label types include evaluation rating and RLHF-style judgments; data handled is text and you will submit solutions and ratings via the project’s labeling software (OTHER).

• Employment type: Contractor, Part-time
• Time commitment: Less than 20 hours per week
• Pay: PAY_PER_HOUR — $45/hour (pay range indicated: $10–$45/hr)
• Data type and tasks: TEXT; label types EVALUATION_RATING and RLHF

What you'll do

Create and verify engineering problems and Python solutions that models will learn from and be evaluated against. Problems may demand numerical iteration, multi-step reasoning, and simulation-style workflows.

Work spans core mechanical and applied topics; you will deliver well-documented Python code and clear solution write-ups appropriate for model training and human evaluation.

• Design computational problems across thermodynamics, fluid mechanics, heat transfer, solid mechanics, dynamics & vibrations, mechanical design, manufacturing processes, and CAE/FEA/CFD concepts.
• Implement verified solutions in Python (numerical methods, iterative solvers, simulation/analysis scripts).
• Provide clear step-by-step explanations, assumptions, and result verification that can be used as ground truth for model training and RLHF evaluations.

Requirements

You must meet one of the education/experience options below and provide evidence of advanced English (C1). Python proficiency is required.

Do not apply if you are located in any of the ineligible countries/regions or U.S. states listed later in this posting.

• Education + experience (required): Either a Bachelor’s degree in a relevant field plus 1+ year relevant engineering practice/research, OR a Master’s or PhD in a relevant field.
• Relevant degrees: mechanical, aerospace, materials, mechatronics/robotics, manufacturing, energy systems engineering, applied physics, applied math, computational mechanics, or closely related fields.
• Experience (if BSc): Minimum 1 year in mechanical/aerospace engineering practice or research (design, analysis, R&D, manufacturing, simulations, or academic research).
• Preferred experience: 3+ years professional experience in thermodynamics, fluids, heat transfer, solid mechanics, dynamics/vibrations, mechanical design, manufacturing, CAD/CAE, FEA, CFD, or thermal systems.
• Python (required): Demonstrable experience using Python for engineering/scientific computing (examples: NumPy, SciPy, pandas; numerical methods; simulation or optimization scripts).
• Simulation/analysis exposure (nice to have): Experience with CAE/FEA/CFD tools or engineering modeling software such as ANSYS, Abaqus, COMSOL, OpenFOAM, MATLAB/Simulink, or similar.
• Professional credentials (nice to have): PE, Chartered Engineer, or progress toward national licensure/certification.
• English (required): Advanced English (C1). Acceptable proof: an English test score, an English-taught degree, or documented advanced English use in professional/academic work.

Location & eligibility restrictions

This role is global but excludes a specific list of countries, territories, and U.S. states. Applicants located in any excluded area are not eligible to participate in this project.

If you are unsure whether your location is eligible, please check the list below before applying.

• Standard exclusions: Iran, Cuba, North Korea, Syria, Sudan, Venezuela, Myanmar, Russia, Belarus, Palestine, Switzerland, China, Taiwan, Kenya
• USA states excluded: Alaska, Arkansas, California, Connecticut, Delaware, Georgia, Hawaii, Illinois, Indiana, Kansas, Louisiana, Maine, Maryland, Massachusetts, Nebraska, Nevada, New Hampshire, New Jersey, New Mexico, Ohio, Oregon, Tennessee, Utah, Vermont, Washington, West Virginia
• Territories/regions excluded: Antarctica, Aruba, Åland Islands, Saint Barthélemy, Bonaire, Sint Eustatius and Saba, Bouvet Island, Cocos (Keeling) Islands, Democratic Republic of the Congo, Cook Islands, Christmas Island, Western Sahara, Falkland Islands (Malvinas), French Guiana, Guadeloupe, South
• Additional exclusions for this request: Afghanistan, American Samoa, The Bahamas, Curaçao, Czechia, Czech Republic, Côte d'Ivoire, Ivory Coast, Equatorial Guinea, Eritrea, Eswatini, Swaziland, Ethiopia, Ghana, Greece, Guatemala, Guernsey, Hong Kong, Lebanon, Macao, Mauritania, Montserrat, Nicaragua,

Who should apply

Apply if you are an intermediate-to-experienced mechanical engineer or researcher who codes in Python and enjoys translating technical problems into clear, verifiable solutions.

This role suits engineers who can write reliable numerical code, document assumptions and steps, and produce solutions usable as model training data and evaluation references.

• Ideal background: BSc + 1 year of relevant engineering practice or MSc/PhD in a relevant field.
• Comfortable with numerical methods and producing reproducible, well-documented Python scripts.
• Strong written English and the ability to explain step-by-step reasoning clearly.

How to apply and what to expect

Applying through OpenTrain is quick: create a free account, complete your profile with education and experience, and submit your application for this listing.

If selected, you'll receive project instructions and evaluation criteria. You will submit problems, Python-based solutions, and any required verification materials through the project's platform. Compensation is hourly under contractor terms.

• Application items to prepare: degree/certificates, résumé/CV highlighting relevant engineering experience, examples of Python numerical or simulation code (if available), and proof of advanced English (C1).
• Typical platform workflow: receive tasks, submit solutions and explanations, participate in review/evaluation steps as required by the project.