Mechanical Engineering QA Lead

OpenTrain AI; Northern (KY); Hybrid; USD 83,000 - 124,000; Part time

Job summary

OpenTrain AI is seeking an intermediate-level Mechanical Engineering Quality Assurance Lead to review AI-generated mechanical engineering content and trainer QA work. You will assess technical accuracy, calculations, unit consistency, and rubric adherence.

This part-time contractor role requires strong English communication, a mechanical engineering degree, and 3+ years of experience. You will provide precise written feedback, help onboard new team members, and coordinate with remote trainers to

Qualifications

• Degree in Mechanical Engineering or closely related field.
• Strong English communication for detailed feedback and collaboration.
• At least 3 years of professional mechanical engineering experience.
• Ability to identify flawed calculations and unsafe recommendations.

Responsibilities

• Review AI-generated mechanical engineering content and trainer QA work.
• Evaluate work against project rubrics and guidelines.
• Provide clear written feedback on accuracy, reasoning, and formatting.
• Communicate workflow updates and quality expectations to trainers and QAs.
• Support onboarding and maintain project documentation for engineering projects.
• Help improve quality processes across remote teams.
• Flag unsafe or misleading engineering recommendations when safety is affected.

Skills

Mechanical engineering

Technical feedback

Rubric-based judgment

English communication

Education

Bachelor's degree in Mechanical Engineering

Tools

CAD

SolidWorks

MATLAB

Python

ANSYS

Job description

About OpenTrain

OpenTrain AI is the hiring and contracting organization for this role. OpenTrain is the #1 platform for finding and building careers in AI training and data labeling, helping people discover projects, build a professional profile, and apply for work that shapes modern AI systems.

• Remote contract opportunity for candidates located in the United States
• Part-time schedule of 20+ hours per week
• Compensation of $75 USD per hour

About AI Training Work

AI training is the human work behind better artificial intelligence. Specialists review model outputs, evaluate reasoning, identify errors, and provide structured feedback so AI systems can produce more accurate, useful, and responsible results.

In this project, your mechanical engineering expertise will help assess technical AI outputs and the people reviewing them. Your judgment can improve how AI handles engineering explanations, calculations, recommendations, and safety-sensitive content.

• Work directly on evaluation and human-feedback tasks for AI development
• Use your professional expertise to identify technical and safety issues
• Contribute to a fast-growing field with flexible remote work

The Role

OpenTrain AI is seeking an intermediate-level Mechanical Engineering Quality Assurance Lead to review AI-generated mechanical engineering content and trainer QA work. You will assess technical accuracy, calculation correctness, clarity, safety, unit consistency, formatting, and adherence to detailed project rubrics.

The role also includes providing precise written feedback, identifying recurring quality issues, supporting onboarding, maintaining project documentation, and helping remote technical teams apply consistent standards across mechanical engineering AI training projects.

• Role type: Part-time contractor
• Location: United States
• Language: English
• Experience level: Intermediate
• Time requirement: 20+ hours per week
• Pay: $75 USD per hour

What You'll Do

You will combine engineering judgment with structured quality review. Your feedback should make technical issues understandable and actionable for trainers, QAs, and project teams.

• Review AI-generated mechanical engineering explanations, calculations, design recommendations, diagrams, and problem-solving steps.
• Evaluate trainer and QA work against project guidelines and project-specific rubrics.
• Provide clear written feedback on technical accuracy, reasoning, standards awareness, formatting, and instruction-following.
• Communicate workflow updates, quality expectations, and review standards to trainers and QAs.
• Support onboarding and maintain documentation for mechanical engineering projects.
• Help improve quality processes across remote technical teams.
• Flag unsafe, misleading, or overconfident engineering recommendations when safety may be affected.

Required Qualifications

Applicants should have a strong mechanical engineering foundation and professional experience applying engineering concepts in practice, education, design, manufacturing, review, or related work. Detailed technical communication and consistent rubric-based judgment are essential.

• Degree in Mechanical Engineering, Aerospace Engineering, Mechatronics, Manufacturing Engineering, or a closely related field.
• Strong English communication skills for detailed technical feedback and team coordination.
• At least 3 years of professional experience in mechanical engineering, product design, manufacturing, R&D, systems engineering, CAD, simulation, technical review, or engineering education.
• Strong understanding of mechanics, thermodynamics, fluid mechanics, heat transfer, machine design, materials, manufacturing processes, dynamics, statics, and engineering drawing interpretation.
• Ability to identify flawed calculations, missing units, unsafe recommendations, poor reasoning, and incomplete explanations.
• Ability to evaluate technical work for accuracy, clarity, safety, unit consistency, formatting, and rubric adherence.

Preferred Experience

Experience with AI training, data annotation, prompt or response evaluation, technical content QA, or rubric-based large language model evaluation is helpful but not required. Familiarity with engineering software and remote team coordination can also support success in this role.

• Familiarity with CAD, FEA/CAE, MATLAB, Python, SolidWorks, AutoCAD, ANSYS, Fusion 360, or similar tools.
• Experience leading or supporting remote teams of trainers, annotators, reviewers, technical writers, or QAs.
• Comfort working with Discord, Google Sheets, Google Docs, trackers, dashboards, and project management systems.
• Experience with AI training, rubric-based evaluation, or technical quality assurance.