Qa engineer • everett wa
AI data lab for training frontier models and evaluating AI agents.Experts contribute their diverse subject matter knowledge across domains such as finance, healthcare, STEM engineering, and more.AI... Show more
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AI data lab for training frontier models and evaluating AI agents.Experts contribute their diverse subject matter knowledge across domains such as finance, healthcare, STEM engineering, and more.AI... Show more
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AI data lab for training frontier models and evaluating AI agents.Experts contribute their diverse subject matter knowledge across domains such as finance, healthcare, STEM engineering, and more.AI... Show more
AI data lab for training frontier models and evaluating AI agents.Experts contribute their diverse subject matter knowledge across domains such as finance, healthcare, STEM engineering, and more.AI... Show more
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AI data lab for training frontier models and evaluating AI agents.Experts contribute their diverse subject matter knowledge across domains such as finance, healthcare, STEM engineering, and more.AI... Show more
The average salary range is between $ 87,500 and $ 133,052 year , with the average salary hovering around $ 102,477 year .
mechanical engineering
technical document analysis
problem-solving
written and verbal communication
data analysis
attention to detail
self-management
interdisciplinary collaboration
technical content creation
data quality assurance
micro1 is a data lab that helps AI labs & Enterprises train foundational models and agents. We provide frontier evaluations and reinforcement learning environments to improve LLM capabilities, as well as contextual evaluations to monitor and improve AI agents in enterprise settings.
Our AI recruiting agent sources and vets domain experts for our data platform that enables rapid production of high-quality training data. Our research spans high-value, specialized domains (legal, medical, finance, coding, etc.), where expert-level data is the hardest to produce and most valuable for advanced model performance. Our pipeline performance system ensures both quality and velocity.
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
