Machine Learning Intern

Link to apply

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About Us

We're Matter — a cutting-edge startup advancing geospatial AI and sensors. We capture ultra-high-resolution images of the world, measuring shape, composition, and temperature all at once. Our data accelerates computer vision and geospatial modeling, unlocking new levels of intelligence.
 

The Project

Help us build next-generation machine learning models that autonomously classify hyperspectral data at the sub-pixel scale. Your work will form the foundation of a data engine set to create the evolution Google Maps with advanced geospatial data.

What You'll Do

• Innovate: Develop models for pixel-wise classification on hyperspectral images.
• Learn: Get experienced with remote sensing and hyperspectral imaging.
• Streamline: Create code for easy fine-tuning and inference by auto labeling data.
• Collaborate: Enhance image segmentation code using the latest ML architectures.
• Optimize: Evaluate performance and implement improvements.
• Extend: Build flexible training architectures for rapid labeling and data ingestion.

Your Skills

• Studying or recently graduated in CS, EE, Remote Sensing, or similar.
• Skilled in Python and deep learning frameworks (PyTorch, TensorFlow).
• Experience with or can learn image segmentation and classification.
• Knowledge of Vision Transformers, Self-supervised learning, and advanced ML architectures.
• Able to adapt data loaders and architectures for various data dimensions.
• Understanding of geospatial data processing.
• Problem-solver, great communicator, team player.
• Self-motivated, good at managing time. Entrepreneur mindset.

Great to Have
 

• Experience with multispectral and/or hyperspectral images.
• Experience training and/or fine-tuning vision foundation models.
   

Why Join Us?
 

• Impact: Your work will influence technology used by millions of people and AI.
• Innovation: Work at the forefront of ML and hyperspectral imaging.
• Growth: Gain hands-on experience with cutting-edge tech.
• Mentorship: Collaborate with industry experts.
• Challenge: Tackle exciting problems in geospatial technology.