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Geospatial Machine Learning Engineer - Climate Tech

Geospatial Machine Learning Engineer - Climate Tech

locationLondon, UK
remoteFully remote
PublishedPublished: 11.10.2021
Software Development
Full time
Mid-senior level


πŸ“ Location: London / GMT +/- 5 hours. Can be remote, in office or hybrid.

⏰ Start Date: ASAP. End date: Flexible

What is this Sylvera you speak of? πŸ‘©β€πŸ‘¨

Sylvera is scaling carbon markets with software. Sylvera does this by applying advanced machine learning techniques to satellite data to quantify with unprecedented accuracy the amount of carbon stored in landscapes. We also provide a holistic picture of offset projects, providing people with the information they need to make informed decisions.

By providing transparency, Sylvera intends to underpin functioning voluntary carbon markets. This will allow the market to scale to keep the planet under 2C of warming, and unlock huge co-benefits for nature and society.

Sylvera is backed by world leading investors such as Index Ventures and collaborates with leading institutions such as University College London, UCLA and NASA -JPL. We're on a super steep trajectory over the next couple of years - we want incredible people that are as passionate as we are about the mission and want to build an amazing organisation with us to achieve it.

What’s my impact? πŸ‘©β€πŸ’»πŸ‘¨β€πŸ’»

  • Making material contributions to the core AI engine for carbon offset verification
  • Contribute to development of new machine learning architectures, models and analytics products using geospatial data
  • Contribute to the design and development of pipelines to train, evaluate and deploy machine learning models at scale
  • Working closely with our Earth Observation Engineers to deeply understand the input data that we use, including radar, lidar, microwave etc so we fully exploit the features latent in the data.
  • Shape product and drive revenue, working with our GIS and Data engineering teams to deploy the model and serve customer requests
  • Working alongside our academic partners who are leaders in the earth observation and natural carbon community to push forward the state of the art
  • Publishing our methodology and model validation for open scrutiny by academics, NGO, Governments and customers
  • Supporting, training and hiring other tech team members to ensure awesomeness across the team


What do I need? ❓

  • Strong working knowledge of Python and machine learning frameworks, ideally Pytorch
  • Demonstrated experience in building, validating, and leveraging machine learning models in computer vision and/or earth observation
  • 3+ years of industry experience ( or PhD and 1 year in industry) implementing ML models, ideally on Earth Observation data, otherwise with vision, Radar, LIDAR data.
  • Good foundations of software architecture, object-oriented design
  • Breadth of coverage and capable of making informed decisions about choice of tools, models and approaches
  • To care about the future of the planet - you want to solve climate change in the most high leverage way possible

Brownie Points 🍰

  • Has worked with multispectral passive and/or active satellite data such as SAR or LIDAR
  • Has worked with forest, vegetation or soil data sets
  • MSc or PhD level in relevant area
  • Experience contributing to product development
  • Experience working with developers deploying models in a production environment

Feel free to apply even if you feel unsure about whether you meet every single requirement in this posting. As long as you're a quick learner, and are excited about changing the carbon markets for the better, we're happy to support you as you come up to speed with our tech stack.

Equal Employment Opportunity

Sylvera is an equal opportunity employer: we value diversity. We do not discriminate on the basis of race, religion, colour, national origin, gender, sexual orientation, age, marital status, veteran status, or disability status.


Β£40-Β£90k p.a. 0.15-0.25% stock options.

You also get paid to save the world which is nice.

Years of experience

  • Mid-senior level