Civil Engineering QA Lead - Remote

YO IT Consulting; Berlin; On-site; EUR 40,000 - 70,000; Part time

Job summary

YO IT Consulting is seeking a Civil Engineering Quality Assurance Lead for a remote contract role. This position involves overseeing quality and consistency in civil engineering AI training projects, providing technical evaluations and feedback, and ensuring adherence to quality standards.

The successful candidate will possess a Bachelor's or Master's degree in Civil Engineering, a strong grasp of engineering topics, and experience in managing remote technical teams. This role requires a detail-oriented approach and effective communication skills.

Join us to enhance the quality of AI training data for leading AI companies.

Qualifications

• 3+ years of professional experience in civil engineering or related fields.
• Strong understanding of core civil engineering topics such as soil mechanics and hydraulics.
• Experience with leading remote teams preferred.

Responsibilities

• Spot-check civil engineering items and identify quality issues.
• Evaluate AI-generated explanations and calculations for correctness.
• Create and maintain project documentation, including style guides.

Skills

Strong English communication skills

Attention to detail

Civil engineering expertise

Education

Bachelor’s or Master’s degree in Civil Engineering or related field

Tools

AutoCAD

Civil 3D

Revit

Python

MATLAB

Job description

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. 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 elevate 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.