Chemical Engineering QA Lead for AI Training Projects
Overview
Work remotely as a contract specialist guiding chemical engineering AI training projects. You will review AI-generated engineering content for accuracy, unit integrity, and safety awareness. Maintain rubrics, style guides, and onboarding materials to keep distributed teams aligned. Strong chemical engineering expertise and precise English communication keep QA consistent across remote contributors.
What You'll Do10
- 1Quality monitoring: Spot-check chemical engineering items, identify quality issues, and provide ongoing feedback through Discord DMs, escalating recurring or critical issues.
- 2Technical review: Assess AI-generated explanations, process calculations, and control strategies for correctness and clarity, focusing on mass and energy balances, thermodynamics, and process control.
- 3Trainer and QA communication: Notify trainers and QAs about new item guidelines, project changes, workflow updates, quality expectations, and chemical-engineering review standards via Discord and Google Sheets.
- 4Question handling: Respond to trainer/QA questions with clear guidance around engineering assumptions, units, formulas, balances, and rubric interpretation.
- 5Activation management: DM inactive contributors, encourage activation, track follow-ups, and flag availability issues using Discord.
- 6Documentation: Create and maintain chemical engineering project docs, including style guides, FAQs, and onboarding materials.
- 7Onboarding and training: Schedule and run onboarding calls with trainers and QAs to explain project expectations, rubrics, and quality standards.
- 8Quality alignment: Ensure trainers and QAs apply engineering guidelines consistently as projects evolve.
- 9Risk and safety review: Flag unsafe or misleading engineering recommendations, especially where process safety may be affected.
- 10Process improvement: Identify recurring quality gaps and propose scalable QA workflows for chemical engineering AI training projects.
Requirements10
- 1Bachelor’s or Master’s degree in chemical engineering, process engineering, biochemical engineering, materials engineering, or petroleum engineering (or closely related field).
- 2Strong command of English to follow guidelines and provide clear feedback in English.
- 33+ years of professional experience in related workflows such as chemical engineering, process engineering, plant operations, or R&D.
- 4Solid understanding of core topics including mass and energy balances, thermodynamics, process control, and reaction engineering.
- 5Ability to evaluate engineering content against detailed rubrics and identify issues.
- 6Familiarity with common tools/workflows like Aspen Plus, Aspen HYSYS, Python, and Excel modeling is preferred, with exposure to PFDs and P&IDs advantageous.
- 7Experience leading or supporting remote teams of trainers and QAs is strongly preferred.
- 8Comfortable working in fast-moving remote environments using Discord, Google Sheets, and Google Docs.
- 9Highly detail-oriented with the ability to maintain style guides, FAQs, trackers, onboarding materials, and calibration tasks.
- 10Experience with AI training, data annotation, large language models, and rubric-based LLM evaluation is a strong plus.
Who Should Apply
Ideal candidates are mid-senior level chemical engineering professionals with hands-on experience in process design and AI training data QA. You have 3+ years in related roles and can communicate clearly in English while coordinating a distributed team. You excel at documenting decisions with style guides and rubrics and can uphold quality standards across remote contributors. This role may not fit for those lacking deep domain knowledge in core chemical engineering topics or with limited experience in remote QA leadership.
Salary Insight
Hourly pay: $105/hr. This rate aligns with mid-senior level remote chemical engineering QA leadership in AI training projects.
Location
Required Skills
Application Tip
In your application, include a concise example of a rubric-based evaluation you designed for LLM QA and share a link or attachment to a QA tracker or sample documentation that demonstrates process improvements.
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