Civil Engineering QA Lead - Remote
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About the Role
Job Title: Civil Engineering Quality Assurance Lead Job Type: Contract Location: Remote About This Role In this hourly, remote contractor role, you will work as a Civil Engineering Quality Assurance Lead to oversee quality, consistency, and trainer performance across civil engineering AI training projects.
Key Skills for This Role
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About This Role
- In this hourly, remote contractor role, you will work as a Civil Engineering Quality Assurance Lead to oversee quality, consistency, and trainer performance across civil engineering AI training projects.
- You will review AI-generated civil 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, standards awareness, unit consistency, safety considerations, 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 civil 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 a fast-growing AI Data Services company delivering training data for many of the world’s largest AI companies and foundation-model labs.
- Your civil 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.
- Important: There is no immediate project for this role; however, if qualified, you will be among the first experts we reach out to when relevant opportunities arise.
- This will also provide you with access to future projects available through our expert network.
- Your profile
- Bachelor’s or Master’s degree in Civil Engineering, Structural Engineering, Geotechnical Engineering, Transportation Engineering, Environmental Engineering, Construction 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 civil engineering, structural design, geotechnical engineering, transportation planning, construction management, infrastructure design, water resources, environmental engineering, technical review, engineering education, or related workflows.
- Strong understanding of core civil engineering topics such as statics, structural analysis, reinforced concrete, steel design, soil mechanics, foundations, hydraulics, hydrology, transportation systems, surveying, construction materials, and engineering drawing interpretation.
- Ability to evaluate engineering content against detailed rubrics and identify issues such as incorrect assumptions, flawed calculations, missing units, unsafe recommendations, code/standards hallucinations, incomplete explanations, or unrealistic design guidance.
- Familiarity with common civil engineering tools or workflows such as AutoCAD, Civil 3D, Revit, ETABS, SAP2000, STAAD.Pro, SAFE, HEC-RAS, HEC-HMS, ArcGIS/QGIS, Bluebeam, Excel, MATLAB, or Python is preferred.
- Experience leading or supporting remote teams of trainers, annotators, reviewers, engineers, technical writers, or QAs is strongly preferred.
- 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.
- Experience with AI training, data annotation, large language models, prompt/response evaluation, technical content QA, or rubric-based LLM evaluation is a strong plus.
Key Responsibilities
- Quality monitoring: Spot-check civil engineering items, identify quality issues, provide ongoing feedback through DMs, and escalate recurring or critical issues.
- Technical review: Evaluate AI-generated engineering explanations, structural calculations, geotechnical reasoning, construction guidance, infrastructure recommendations, 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 civil-engineering-specific review standards.
- Question handling: Respond to trainer/QA questions clearly and promptly, especially around engineering assumptions, units, formulas, loads, factors of safety, design constraints, 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 civil 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 civil-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 structures, infrastructure, construction safety, foundations, transportation systems, drainage, or public safety may be affected.
- Process improvement: Identify recurring quality gaps, propose workflow improvements, and help build scalable QA processes for civil engineering AI training projects.
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