Intern Deep Learning Engineer - Geospatial

🌍 Deep Learning Engineer Intern - Geospatial Data 🌍

🚀 Join us on a mission to heal the planet, one pixel at a time! 🚀

Location: 🌎 Remote with 3 hours overlap with Amsterdam time

Pay: 💶 €800/month for a minimum of 32 hours per week

Duration: 🕒 6+ months

Who We Are

Welcome to Spatialise, where we blend geospatial magic, machine learning wizardry, and a deep commitment to sustainability to shape the future of agriculture! 🌱✨

Founded by a dream team of innovators from Wageningen University, ex-IBM leadership, machine learning experts from Bain & BCG, and researchers from the University of Utrecht, we’re on a mission to empower farmers, NGOs, and corporations to regenerate soils and nurture our planet. 💪🌍

What You’ll Be Doing

💡 Get hands-on: Collaborate with founders and tech leaders to develop deep learning models that impacts our earth.

🛰️ Satellite sleuthing: Work with cutting-edge data like Sentinel1, Sentinel2 and Modis to develop next-gen solutions for monitoring soil health indicators like Soil Organic Carbon.

🤖 ML magic: Build and refine machine learning models using Python, GeoPandas, PyTorch, PyTorch Geometric and TorchGeo.

☁️ Cloud diving: Deploy and scale models on GCP to create real-world impact.

🎓 Learn and grow: Supercharge your skills with our Acceleration Program, designed to make you a geospatial ML superstar! 🌟

Who You Are

• A final-year Bachelor’s or Master’s student in Computer Science, Artificial Intelligence, Data Science, or an equivalent field.
• Passionate about solving real-world problems and eager to learn.
• Skilled in Python and have experience with deep learning with Pytorch, Tensorflow, Jax, or similar.
• Skilled in the mathematics behind deep learning, including linear algebra and calculus.
• Demonstrated end-to-end ownership in your tasks and enjoy working on complex problems.
• Enjoy converting research ideas in quick actionable output that can directly be tested for project.

Bonus points if you have

• Good experience with the tools mentioned in the MIT Missing Semester of Your CS Education course (https://missing.csail.mit.edu/). 🛠️
• Experience with Google Earth Engine, Landsat, or Sentinel data. 🛰️
• Familiarity with Git and cloud platforms like GCP. 🌩️

Why Join Us?

🌟 Make an impact: Build technology that empowers farmers, NGOs, and corporations to cultivate healthier soils and drive sustainable change.

👩‍🔬 Work with the best: Collaborate with an all-star team of founders and leaders from top institutions and industries.

💪 Boost your career: Join our Acceleration Program to level up as a geospatial machine learning pro.

⏳ Fast-track hiring: Project review ➡️ 30-min interview ➡️ 1-week paid work trial = 🚀 start making a difference!