Intern

About the Role

• Hands-on, project-based internship supporting AI and machine learning development within a geospatial environment.
• Ideal for students currently enrolled in a Computer Science, Engineering, or GIS-related undergraduate program.
• Provides exposure to remote sensing, spatial analytics, machine learning, and GIS software tools.
• This is an in-office position.

Key Responsibilities

• Assist in the preparation and cleaning of geospatial data, including satellite imagery, lidar, and vector datasets.
• Support the development and evaluation of AI/ML models for geospatial applications (e.g., feature extraction, classification, image segmentation).
• Work with tools such as Python, ArcGIS, QGIS, Solv3D, and cloud-based geospatial platforms.
• Help build and test geospatial workflows and automation pipelines.
• Participate in data visualization tasks, creating maps and summaries of AI model results.
• Document code, processes, and findings in an organized and reproducible manner.
• Collaborate with lidar analysts, GIS analysts, and data scientists on active projects.
• Attend team meetings and contribute to technical discussions, brainstorming, and review sessions.

Requirements & Tech Stack

• Python
• ArcGIS
• QGIS
• Solv3D
• Cloud-based geospatial platforms
• Git / GitHub (preferred)
• GDAL, rasterio, geopandas (preferred)
• scikit-learn, TensorFlow, PyTorch (preferred)

Qualifications

• Current enrollment in a 4-year degree program in Computer Science, Computer Engineering, Geospatial Science, Data Science, or a related STEM field.
• Completed coursework in programming (Python preferred), data structures, or machine learning fundamentals.
• Interest in geospatial data, remote sensing, and AI/ML applications.
• Ability to work independently and follow technical guidance.
• Strong attention to detail and willingness to learn.
• Good written and verbal communication skills.
• Familiarity with GIS platforms such as ArcGIS Pro, QGIS, or similar (preferred).
• Experience working with geospatial data libraries (e.g., GDAL, rasterio, geopandas) (preferred).
• Exposure to machine learning libraries such as scikit-learn, TensorFlow, or PyTorch (preferred).
• Experience using Git or GitHub for version control (preferred).
• Previous internships, personal projects, or coursework related to spatial analysis, AI, or remote sensing (preferred).

Internship Details

• Hours: Part-time or full-time depending on availability and academic schedule.
• Mentorship: Paired with a technical mentor, regular feedback, and career development guidance.
• Work Environment: Office setting. Opportunity to work on real client or research projects in the geospatial intelligence and engineering domain. Collaborative and inclusive team environment. Emphasis on learning, experimentation, and professional growth.
• Paid internship.