Staff Pavement Engineer

Applied Research Associates, Inc.•Ventura, CA

53d•$75,000 - $85,000•Onsite

About The Position

ARA is seeking a staff pavement engineer with a focus on pavement engineering and data science to join our growing Transportation Division. This is a full-time position in our Ventura, CA office. At ARA, you'll be part of a collaborative, innovative, and mission-driven team. We support engineers who want to grow technically, contribute to high-quality deliverables, and evolve into subject matter experts. The staff pavement engineer will help transform pavement evaluation data into actionable engineering and asset-management decisions. You'll work at the intersection of nondestructive pavement testing, data science/AI, and mechanistic-empirical pavement design, contributing to research and consulting projects for state DOTs, local agencies, and federal sponsors. This role is ideal for someone with hands-on experience using Traffic Speed Deflection Devices (TSDD) and Falling Weight Deflectometer (FWD) data and a passion for building data-driven and AI-based tools that improve infrastructure decisions.

Requirements

BS in Civil Engineering (Pavements/Transportation/Geotechnical) or closely related discipline with 2+ years of relevant experience or advanced degree.
Academic, internship, and/or professional experience in areas such as pavement design, pavement condition evaluation, pavement management, and pavement preservation.
Direct experience working with TSDD and FWD data (collection, processing, interpretation, or model development).
Demonstrated capability in data-driven pavement engineering (e.g., forecasting, network optimization, treatment selection, or decision support).
Clear, concise technical writing and presentation skills.
Strong analytical, organizational, and problem-solving abilities.
Demonstrated ability to work independently and collaboratively on multi-disciplinary teams.

Nice To Haves

Knowledge of PMED and mechanistic-empirical pavement design workflows (traffic, climate, materials, calibration/interpretation).
Proficiency in Python for data wrangling, analysis, and model development; familiarity with R a plus.
Experience with GIS (ArcGIS/QGIS) and geospatial data workflows.
Practical use of LLMs/AI to solve pavement engineering problems.
Familiarity with pavement management system tools/workflows; exposure to large-scale network datasets and agency decision processes.
Prior research experience, including publications or presentations.
EIT (or ability to obtain within 6 months); intent to pursue PE licensure within ~2 years.
Familiarity writing proposals or grants.

Responsibilities

Import, process, and analyze TSDD, FWD, ground penetrating radar (GPR), and other pavement condition datasets; develop quality-control workflows and automate repeatable analytics.
Derive network- and project-level structural indicators, remaining life estimates, and treatment triggers; support pavement management system strategy development.
Build predictive and mechanistic-informed models (e.g., performance forecasting, transfer functions, layer moduli backcalculation pipelines) using Python and modern machine learning frameworks.
Leverage large language models/AI to accelerate data cleaning, metadata extraction, report drafting, proposal writing, and interactive Q&A over large engineering document sets and databases.
Conduct applied research and consulting work in pavement and geotechnical engineering, including design, analysis, materials testing, and condition evaluation.
Integrate and visualize results in geographic information systems (GIS) and dashboards for internal teams and client stakeholders.
Support mechanistic-empirical pavement design tasks; prepare inputs and interpret results from AASHTOWare Pavement ME Design (PMED).
Contribute to proposals, technical reports, and client presentations; present findings at industry conferences and other venues.
Participate in field investigations (periodic travel) to support data collection, validation, and client coordination.
Collaborate with senior engineers and subject matter experts while developing toward independent technical leadership on research and consulting workstreams.