Machine Learning Engineer – Climate tech
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What we are looking for
CarbonFarm’s work combines remote sensing data, agricultural data and machine learning. We are 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.
Responsibilities
- 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
Requirements
- 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 focused 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
Would be nice
- Familiarity working in an agile environment
- Experience using cloud-based computing (GCP or similar)
- Previous exposure to the remote sensing domain
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