Note: The job is a remote job and is open to candidates in USA. YO IT Consulting is a fast-growing AI Data Services company delivering training data for many of the world’s largest AI companies and foundation-model labs. They are seeking a Chemical Engineering Quality Assurance Lead to oversee quality and consistency across chemical engineering AI training projects, ensuring that engineering training data is accurate and aligned with client expectations.

Responsibilities

• Quality monitoring: Spot-check chemical engineering items, identify quality issues, provide ongoing feedback through DMs, and escalate recurring or critical issues
• Technical review: Evaluate AI-generated engineering explanations, process calculations, mass/energy balances, reaction engineering solutions, separation process reasoning, process-control explanations, diagrams/descriptions, and problem-solving workflows for correctness and clarity
• Trainer and QA communication: Update trainers and QAs on Discord about new item guidelines, project changes, workflow updates, quality expectations, and chemical-engineering-specific review standards
• Question handling: Respond to trainer/QA questions clearly and promptly, especially around engineering assumptions, units, formulas, balances, reaction conditions, process constraints, safety concerns, standards references, and rubric interpretation
• Trainer/QA activation management: DM contributors who are inactive or not working, encourage activation, track follow-ups, and flag availability issues when needed
• Documentation: Create and maintain chemical engineering project documentation, including style guides, trackers, FAQs, quality notes, examples, honeypots, calibration tasks, and onboarding materials
• Onboarding and training: Schedule and run onboarding/training calls with trainers and QAs to explain project expectations, workflows, rubrics, quality standards, and chemical-engineering-specific review requirements
• Quality alignment: Ensure all trainers and QAs apply engineering guidelines consistently and understand updates as projects evolve
• Risk and safety review: Flag unsafe, misleading, or overconfident engineering recommendations, especially where chemicals, process conditions, reactions, plant operations, pressure systems, thermal hazards, environmental impact, or worker safety may be affected
• Process improvement: Identify recurring quality gaps, propose workflow improvements, and help build scalable QA processes for chemical engineering AI training projects

Skills

• Bachelor's or Master's degree in Chemical Engineering, Process Engineering, Biochemical Engineering, Materials Engineering, Petroleum Engineering, or a closely related engineering field
• Strong grasp of the English language to follow project guidelines, communicate with teams, and provide clear technical feedback in English
• 3+ years of professional experience in chemical engineering, process engineering, plant operations, process design, R&D, manufacturing, process safety, technical review, engineering education, or related workflows
• Strong understanding of core chemical engineering topics such as mass and energy balances, thermodynamics, fluid mechanics, heat transfer, mass transfer, reaction engineering, separation processes, process control, transport phenomena, and process design
• Ability to evaluate engineering content against detailed rubrics and identify issues such as incorrect assumptions, flawed calculations, missing units, unsafe recommendations, incomplete mass/energy balances, hallucinated standards, or incomplete explanations
• Comfortable working in fast-moving remote environments using tools such as Discord, Google Sheets, Google Docs, trackers, dashboards, and project management systems
• Highly detail-oriented and organized, with the ability to maintain style guides, FAQs, trackers, onboarding materials, honeypots, calibration tasks, and other quality documentation
• Familiarity with common chemical engineering tools or workflows such as Aspen Plus, Aspen HYSYS, MATLAB, Python, CHEMCAD, COMSOL, process simulators, PFDs, P&IDs, Excel modeling, or process safety documentation
• Experience leading or supporting remote teams of trainers, annotators, reviewers, engineers, technical writers, or QAs
• Experience with AI training, data annotation, large language models, prompt/response evaluation, technical content QA, or rubric-based LLM evaluation

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