Note: The job is a remote job and is open to candidates in USA. SME Careers is a fast-growing AI Data Services company and subsidiary of SuperAnnotate, delivering training data for many of the world’s largest AI companies. They are seeking a Civil Engineer Quality Assurance Lead (QAL) to oversee quality and consistency across civil engineering AI training projects, reviewing AI-generated content and ensuring adherence to project guidelines.

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

Skills

• 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
• 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 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
• 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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