Computer engineering • laredo tx
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The average salary range is between $ 51,525 and $ 146,429 year , with the average salary hovering around $ 77,924 year .
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
