About the job
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 Talent connects elite candidates with leading startups in the US and EU, representing the top 1% of talent globally. Our clients have collectively raised over $5 billion, backed by industry giants such as OpenAI, a16z, and Founders Fund. We seek driven candidates who excel in their fields and can build long-term partnerships with our clients.
Discover our mission and values: WATCH HERE
Why Work with Us?
At Pearl, we offer more than just job placements; we provide access to exceptional opportunities that allow you to collaborate with visionary founders from the US and EU. Our focus is on your growth, ensuring you are challenged in your role and laying the foundation for a meaningful career.
About the Company
Our client operates at the forefront of technology, combining data, infrastructure, and machine learning to create large-scale datasets that drive advanced analytics and AI solutions. Their work empowers organizations to make informed decisions through real-world visual and geospatial insights, all within a dynamic and ownership-driven environment where data integrity and execution excellence are paramount.
Role Overview
The Infrastructure Analyst will analyze roadway video and imagery to identify, classify, and document infrastructure conditions, which include pavement distresses (such as cracks, potholes, rutting, and raveling), road assets (like signage, markings, and barriers), and surface conditions. This position ensures scalable, accurate, and consistent data delivery.
This role is particularly suited for civil engineering graduates eager to apply their expertise in pavements, road construction, and infrastructure assessment within a technologically advanced setting. You will establish standards, resolve complex cases, and serve as the quality authority for datasets.
The role involves cross-functional collaboration with Machine Learning and Customer Success teams while leading operational annotation teams. The ideal candidate is decisive, detail-oriented, and thrives in fast-paced environments where speed and accuracy are critical.

