Machine Learning Engineer: Terrestrial Ecosystem Modeling

Machine Learning Engineer: Terrestrial Ecosystem Modeling

Type of offer:

We’re looking for Machine Learning Engineers who are passionate about using ML and AI to bring about ecological restoration on a massive scale. In this role you’ll have the opportunity to work with a world-class team of scientists and engineers, to build and deploy the ecological prediction module that will form the backend to LandOS. Your work will directly impact Earthshot’s methodology for determining how carbon is expected to accumulate in restored ecosystems over time, under different management scenarios. You will aid in designing a modeling system that accurately portrays ecosystem services, and the financial outcomes for landowners that will catalyze ecological restoration on a massive scale, all in a visually delightful way. This will be accomplished through collaboration with scientists to develop a fast and accurate emulator model (statistical model) to describe changes in ecosystems over time, and to develop an AI approach to creating realistic visualizations of the land surface under future scenarios. You will then own the deployment of these models to serve the near-real-time LandOS app that landowners will interact with.


  • BS or MS degree in a technical field (Computer Science, Electrical Engineering, Applied Mathematics, Ecology or related technical discipline or equivalent experience)
  • Experience applying deep learning architectures to real world problems; knowledge of different forms of ANN’s, extensive knowledge of feature engineering, hyperparameter tuning, and other aspects of model development and refinement
  • Experience with software development applied to solving real scientific problems
  • Expertise in Python, R or other commonly used machine learning languages
  • Experience building and deploying enterprise-grade machine learning systems, e.g. in private industry
  • Ability to define and execute projects on a timeline
  • A deep love for nature and desire to help build a more integrated future for humanity within it
Location: Remote
Organization: Earth Shot Labs
Deadline: April 30, 2022
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