Requirements
QUALIFICATIONS — REQUIREDBachelor's degree in Civil Engineering, Transportation Engineering, or a related disciplineStrong visual attention to detail, with the ability to maintain consistency across high-volume repetitive reviewComfort working for extended periods within image-review software environmentsAbility to apply written standards consistently and document deviations clearlyStrong written and verbal communication skills in EnglishATTRIBUTES & WORKING STYLEMethodical and consistent — able to make the same judgment callComfortable operating within defined standards, and flags genuine edge cases rather than guessingCollaborative approach to feedback, both giving and receivingOwnership mindset toward deliverable quality
Description
We are hiring a Pavement Visual Inspection Engineer to join our Pavement Assessment team.This is a hands-on, detail-driven role at the centre of our data quality chain. You will spend most of your time in imagery — labelling pavement distresses, correcting the output of our AI detection models, and performing structured visual inspection on assigned road networks. Your work directly shapes two things at once: the condition data we deliver to clients, and the training datasets that make our models better on the next project.This role suits someone who takes genuine satisfaction in getting the call right, holds a standard consistently over thousands of segments, and would rather flag a hard case than guess at it.CORE RESPONSIBILITIESWork within the annotation and labelling team to expand and refine model training datasets and deliverable dataValidate AI-generated defect detections against source imagery — confirming, correcting, or rejecting each output per the assignment criteriaIdentify and classify pavement distresses by type, severity, and extent in accordance with internal guidelinesVisually review collected pavement-view roadway imagery to assess surface condition across assigned project areasPerform independent visual inspection on statistically sampled road segmentsAssess surface type classification (asphalt, concrete, gravel, composite) and confirm the correct defect taxonomy is applied for each surfaceReview segment-level condition scores for consistency with the underlying imagery and defect recordDetect and report imagery quality issues — occlusion, glare, motion blur, water or snow coverage, insufficient lighting — that prevent reliable assessment