Chemical Engineer Quality Assurance Lead; QAL

Pay: up to $105/hour

In this hourly, remote contractor role, you will work as a Chemical Engineering Quality Assurance Lead (QAL) to oversee quality, consistency, and trainer performance across chemical engineering AI training projects. You will review AI-generated chemical engineering content and trainer/QA work, evaluate output quality against project guidelines, provide precise written feedback, and ensure that all contributors follow the expected quality standards.

You will assess work for technical accuracy, engineering reasoning, calculation correctness, unit consistency, process safety awareness, standards awareness, clarity, formatting, instruction-following, and adherence to project-specific rubrics. You will spot recurring quality issues, communicate updates to trainers and QAs, support onboarding, maintain documentation, and help activate contributors who are not working consistently. This role requires strong chemical engineering expertise, strong English communication skills, excellent attention to detail, structured communication, and the ability to manage quality workflows across remote technical teams.

This role is with SME Careers, a fast-growing AI Data Services company and subsidiary of Super Annotate, delivering training data for many of the world’s largest AI companies and foundation-model labs. Your chemical engineering quality leadership will directly help improve the world’s premier AI models by ensuring that engineering training data is accurate, logically sound, clearly explained, well-documented, and aligned with client expectations.

Selection process involves an AI interview, a domain-specific task, and an interview with a recruiter.

• 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.