Mechanical Engineer

• Job Description
• Mechanical Engineer
• Contractor
• Remote

Rate

20$-50$ per task

Job Summary

In this role, you'll apply your expertise to help train next-generation AI systems. Your work will shape how models learn, reason, and perform through high-quality, real-world input. No prior experience in AI is required — your domain knowledge is what matters.

Key Responsibilities

• • Analyze technical documents and extract key information relevant to your domain expertise
• • Generate, review, and refine questions and solutions based on real-world mechanical engineering scenarios
• • Collaborate with multidisciplinary teams to ensure accuracy and depth of data and analysis
• • Provide detailed written and verbal feedback on document clarity, technical accuracy, and effectiveness
• • Apply advanced subject matter knowledge to evaluate and solve complex engineering problems
• • Work independently, managing your time to deliver high-quality outputs with minimal supervision
• • Contribute to the continuous improvement of data quality and project processes

Required Skills and Qualifications

• • 2-6 years of professional or academic experience in mechanical engineering or a closely related field (lab, workplace, or academia)
• • Strong reading comprehension skills, with proven ability to analyze and interpret technical documents
• • Exceptional English communication skills, both written and verbal
• • Demonstrated experience in creating or evaluating technical content, such as reports, studies, or problem sets
• • Ability to work independently and efficiently, with excellent self-management
• • High attention to detail and commitment to data accuracy
• • Experience interfacing with technical documentation and performing analytical tasks

Preferred Qualifications

• • Advanced degree (Master’s or PhD) in Mechanical Engineering, Chemistry, Physics, or Biology
• • Previous experience contributing to interdisciplinary or research-driven projects
• • Familiarity with data-driven analysis or training data preparation