Mechanical Engineering Subject Matter Expert (Remote)

About OpenTrain

OpenTrain is the #1 platform for finding and building careers in AI training and data labeling. We help freelancers discover specialized AI training projects, build a persistent portfolio of work, and grow a durable freelance career focused on shaping how AI systems learn.

As the hiring organization for this role, OpenTrain offers structured, remote work that lets you convert engineering expertise into direct impact on AI systems and a track record you can show across projects.

Why AI-training work matters

AI training (data labeling and human feedback) is the human side of building intelligent systems — people prepare, review, and rate examples that teach models to reason and respond. This work is remote, flexible, and directly shapes how state-of-the-art models behave.

Mechanical engineering reviewers are critical: your technical judgment improves model accuracy on design, analysis, manufacturability, and reliability topics that engineers and products depend on.

The role

OpenTrain is hiring a Mechanical Engineering Subject Matter Expert to evaluate AI-generated technical content and provide high-quality engineering feedback. You will analyze advanced engineering problems, assess AI solutions for accuracy and feasibility, and write detailed rationales to guide model training.

This is a part-time, contractor role (20+ hours/week), fully remote and open worldwide. Compensation is paid hourly at $80–$100 USD per hour.

• Employment: Contractor, Part-time
• Time requirement: 20+ hours/week
• Pay: $80–$100 USD per hour
• Location: Remote — worldwide (English required)

What you'll do day-to-day

Work on real-world engineering prompts and AI responses across core mechanical disciplines. Evaluate technical content for correctness, clarity, completeness, manufacturability, and compliance with accepted engineering practice.

• Analyze complex problems in mechanics, thermodynamics, fluids, heat transfer, vibrations, and manufacturing.
• Review and rate AI-generated answers; correct errors and suggest improvements.
• Write detailed written rationales that explain engineering decisions and assumptions.
• Assess feasibility, manufacturability, reliability, and standards considerations.
• Collaborate remotely with a multidisciplinary team to iterate on instructions and evaluations.

Requirements

You must be able to apply deep mechanical engineering knowledge and practical judgment to review and improve technical content. Strong written English is required because feedback must be precise and actionable.

• Deep knowledge across mechanics, thermodynamics, fluid mechanics, heat transfer, vibrations, and manufacturing.
• Ability to evaluate AI-generated technical answers for accuracy, clarity, and completeness.
• Experience applying manufacturability and reliability judgment to design scenarios.
• Strong written and verbal English communication for detailed technical feedback.
• Background in mechanical design, product development, analysis, or R&D (preferred).

Who should apply

This role is a fit for mechanical engineers, designers, analysts, or R&D professionals who enjoy reviewing technical work and writing clear engineering rationales. Entry-level candidates with strong fundamentals and practical experience are welcome.

You do not need prior annotation experience; we value engineering knowledge, attention to detail, and the ability to explain decisions clearly.

• Entry-level through senior engineers who want flexible, impactful freelance work.
• Professionals seeking part-time, remote work that contributes to how AI understands engineering.

How the work is structured

Tasks include question-answering and evaluation/rating of text responses. You will use provided guidelines to score, correct, and justify model outputs. Work is assigned through OpenTrain and tracked to build your profile and portfolio.

All factual role details (hours, pay range, and required skills) are as stated above. If selected, you will receive task instructions, scoring rubrics, and examples to align your reviews with project goals.

• Label types: Question answering and evaluation/rating of text outputs.
• Data type: Text-based engineering prompts and responses.
• Language: English required.
• Worldwide applicants accepted; collaboration is remote.