AI & Data Engineering Intern

Role Overview

We are seeking an AI & Data Engineering intern to support the development of Pixxel's geospatial intelligence platform for maritime and land domain awareness. This platform fuses multi-source satellite imagery and sensor data into a unified operational picture, enabling defense and national security missions to detect, monitor, and reason about activity across maritime and land environments. This is a hands-on internship where you will work across the full stack: from data ingestion and preprocessing pipelines, through AI model development, to analyst-facing interfaces that make geospatial intelligence actionable.

Responsibilities

• Design and build scalable data pipelines for ingestion, preprocessing, and fusion of heterogeneous geospatial data sources.
• Establish consistent data standards and quality control processes across geospatial data feeds.
• Build infrastructure for near-real-time geospatial data workflows.
• Develop monitoring and evaluation tools to assess data pipeline performance.
• Apply transformer architectures to geospatial problems, including object detection and classification.
• Develop and fine-tune vision-language models (VLMs) for natural language reasoning over geospatial data.
• Explore diffusion model approaches for image generation and super-resolution.
• Build multi-modal fusion pipelines combining optical imagery, SAR, and AIS.
• Write clean, efficient, and maintainable Python code.

Requirements

• Currently pursuing or recently completed a Bachelor's degree in Computer Science, Machine Learning, Data Science, Engineering, Physics, or a related field.
• Strong programming skills in Python.
• Solid understanding of deep learning fundamentals, including CNNs, attention mechanisms, and transformer architectures.
• Familiarity with vision-language models (VLMs), diffusion models, or multi-modal learning.
• Practical experience or strong academic coursework in data pipelines or data engineering workflows.
• Basic understanding of the end-to-end machine learning lifecycle.

Skills

• Python
• PyTorch
• TensorFlow
• NumPy
• Pandas