CarbonFarm is a climateTech working on changing the way we grow our food. Our vision? If farming is to become sustainable, it has to make economic sense to the farmer.
By using satellites and AI, we quantify emissions from agriculture so that farmers can get rewarded when they adopt low-emission practices.
Apply through our online form!
CarbonFarm is looking for a climate-passionate 🌳 Machine Learning Engineer to turn terabytes of satellite data into useful insights about farming practices.
Data is at the heart of everything we do, and we have a lot of it! You’ll work with a wide variety of satellite imagery and Internet-of-things data to build a comprehensive understanding of agricultural practices around the world. Our team and partners are spread across dozens of countries, so you’ll need to be a clear technical communicator who’s capable of understanding different points of view.
Collaborate with fellow engineers (ML, AI, Data) to develop models and algorithms that identify how farmers are cultivating their land from satellite and IoT data
Propose and implement new pre-training and fine-tuning approaches for deep models
Work with our chief architect to deploy models in production
Research different approaches in remote sensing from the scientific community
Test and validate models, with a particular focus on temporal/geospatial cross-validation and the mitigation of bias and leakage
Leverage AI tooling to drive rapid iteration, with a strong bias for technical accuracy
Master’s degree or equivalent in data science, computer science, or remote sensing
3+ years of experience with PyTorch (preferred), TensorFlow, or related frameworks
Comfortable with a variety of different database technologies (SQL, OLAP)
Strong foundations in statistics and probability
Flexible and focussed on solutions
Organised and self-sufficient
Fluent in English
Paris-based or willing to relocate, with the ability to work in person with the team
Familiarity working in an agile environment
Experience using cloud-based computing (GCP or similar)
Previous exposure to the remote sensing domain
first call
technical interview
second technical interview
fit and behavioural interview
