Infrastructure Analyst (Roadway Data Specialist) - A251 job opportunity at Pearl Talent.

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Date2026-01-13T17:07:51.841Z bot
Pearl Talent Infrastructure Analyst (Roadway Data Specialist) - A251
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Pattern: Full-time
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Industry Technical / Data Infrastructure Work Arrangement Remote Job Type Full-time Work Schedule Standard business hours with required overlap with US Pacific Time (PST) Locations: LATAM: Mexico City (Mexico), Bogotá (Colombia), São Paulo (Brazil), Buenos Aires (Argentina), Caracas (Venezuela), Honduras (Dominican Republic) Anywhere in LATAM About Pearl Talent Pearl works with the top 1% of candidates from around the world and connects them with the best startups in the US and EU. Our clients have raised over $5B in aggregate and are backed by companies like OpenAI, a16z, and Founders Fund. They’re looking for the sharpest, hungriest candidates who they can consistently promote and work with over many years. Candidates we’ve hired have been flown out to the US and EU to work with their clients, and even promoted to roles that match folks onshore in the US. Hear why we exist, what we believe in, and who we’re building for: WATCH HERE Why Work with Us? At Pearl, we’re not just another recruiting firm—we connect you with exceptional opportunities to work alongside visionary US and EU founders. Our focus is on placing you in roles where you can grow, be challenged, and build long-term, meaningful careers. About the Company Our client is a technology-driven company operating at the intersection of data, infrastructure, and machine learning. They build large-scale datasets that power advanced analytics and AI systems, helping organizations make better decisions using real-world visual and geospatial information. The company operates in a fast-paced, high-ownership environment where data quality and execution excellence are critical to customer success. Role Overview The Infrastructure Analyst is responsible for analyzing roadway video and imagery to identify, classify, and document infrastructure conditions — including pavement distresses (cracks, potholes, rutting, raveling), road assets (signage, markings, barriers), and surface conditions. This role ensures accurate, consistent data delivery at scale. This is an ideal role for civil engineering graduates who want to apply their technical knowledge of pavements, road construction, and infrastructure assessment in a technology-forward environment. You will define standards, resolve ambiguous edge cases, and act as the final quality authority for datasets. The role works cross-functionally with Machine Learning and Customer Success teams while leading operational annotation teams. The ideal candidate is decisive, detail-oriented, and thrives in environments where speed, accuracy, and technical judgment must be carefully balanced. Your Impact Deliver high-quality, customer-ready roadway condition datasets that directly support machine learning performance and infrastructure decision-making Apply civil engineering knowledge to accurately identify and classify pavement distresses and road defects Reduce ambiguity and improve data consistency across large-scale infrastructure assessments Help scale data operations while maintaining strict quality standards Enable faster iteration on ML models and stronger customer satisfaction through accurate ground-truth data Core Responsibilities Roadway Analysis & Data Creation – 40% Analyze roadway video and imagery to identify pavement distresses: cracking (alligator, longitudinal, transverse, block), potholes, rutting, raveling, patching, and other surface defects Classify infrastructure assets including signage, road markings, guardrails, drainage features, and barriers Apply pavement condition assessment methodologies to ensure consistent, accurate classification Own the full data creation lifecycle from video preprocessing through tagging, QA, and final delivery Quality & Standards – 25% Define, document, and continuously refine classification standards for roadway conditions and assets Build and maintain QA frameworks that ensure repeatable, high-quality output Act as the final decision-maker on ambiguous classifications and edge cases Identify recurring quality issues and implement corrective processes Team Leadership – 20% Lead and manage annotation and review teams to meet quality and delivery targets Train team members on pavement distress identification and classification standards Set performance benchmarks and provide clear feedback to maintain consistency Scale output while preserving quality standards Cross-Functional Collaboration – 10% Partner closely with Machine Learning teams to align datasets with real-world infrastructure requirements Work with Customer Success teams to ensure timely and accurate dataset delivery Translate customer needs (transportation agencies, municipalities) into clear internal execution standards Process Improvement – 5% Identify opportunities to automate or streamline tagging, QA, and review workflows Build systems and processes that support growing dataset volume and complexity Must-Haves (Required) Civil Engineering degree or related field (Transportation Engineering, Construction Management, or similar) Knowledge of pavement types, road construction methods, and infrastructure assessment principles Understanding of pavement distress types and classification (cracks, potholes, surface deterioration) Strong attention to detail and ability to make consistent, judgment-based classifications Excellent written and verbal English communication skills Ability to work independently and take ownership of data quality outcomes Strong Preferences Experience with pavement condition assessment, road inspections, or infrastructure surveys Coursework or practical experience in pavement engineering, highway design, or transportation infrastructure Experience with GIS, geospatial data, or mapping tools Familiarity with pavement management systems (PMS) or asset management methodologies Previous experience in data annotation, quality assurance, or technical review processes Nice-to-Haves (Preferred) Experience leading or managing operational teams (annotators, reviewers, or QA) Experience collaborating with technical stakeholders or engineering teams Knowledge of machine learning data labeling concepts Project management experience Tools Proficiency Must-Haves (Required) Google Workspace (Docs, Sheets, Drive) Spreadsheet tools (Google Sheets or Excel) Comfortable learning new software platforms Nice-to-Haves (Preferred) GIS tools (ArcGIS, QGIS, Google Earth) Annotation or labeling platforms Project management tools (Asana, Monday, Jira) Data QA or review tools

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