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