Applied Scientist – Forest Lidar & 3D ML
Core
Automating the segmentation of individual trees from complex terrestrial laser scanning (TLS) point clouds to power Sylvera's Biomass Atlas for carbon accounting.
Role type
Applied Scientist (3D ML & Lidar)
Builds
Production-ready 3D deep learning models for tree instance segmentation and Quantitative Structure Model (QSM) generation.
Domain
Climate tech / Carbon markets / Geospatial analytics / Forest ecology
Deliverable
production ML models
Required skills
3D point cloud processing, machine learning for 3D spatial data, Python, forest ecology domain knowledge
Preferred skills
Experience with early-stage startups, grit, self-starter mindset
Technologies
PDAL, laspy, Open3D, sparse convolutions, PointNet, TreeLearn
Responsibilities
Develop, train, and deploy 3D deep learning models for tree segmentation and QSM generation; Translate experimental ML research into robust, reproducible code; Collaborate with field teams to ensure model outputs align with biological reality; Scope, prototype, and iterate on open applied research problems; Improve existing data products and methods for manual segmentation and quality assurance; Solve complex technical challenges involving point cloud and geospatial data (e.g., forest carbon modeling, uncertainty quantification).
Seniority
Mid-to-Senior, hands-on IC