Mechanical Engineer

**Job Title:** Mechanical Engineer **Job Type:** Contractor **Location:** 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