Research Scientist, Biosphere Models

Design, implement, and train state-of-the-art Geospatial AI models (e.g., multi-modal multi-task) on planetary-scale datasets.

Develop novel approaches for self-supervised or weakly-supervised pretraining to tackle data scarcity in natural environments.

Build and maintain scalable data pipelines for ingesting and processing heterogeneous Earth Observation data.

Lead the technical validation of models against ground truth, contributing to the design of validation campaigns and geospatial annotation strategies.

Collaborate with domain experts to refine model objectives for downstream application domains.

Report and present research findings clearly and efficiently, leading to open-source code releases and scientific publications.

Contribute to team collaborations to meet ambitious research and product goals.

Engage with application and product needs, to inform research and engineering decisions.

BSc, MSc or PhD degree in Computer Science, Machine Learning, Remote Sensing, Geoinformatics, or a related technical field, or equivalent practical experience.

Excellent software engineering skills in Python with a proven ability to build robust and scalable systems.

Proficiency in deep learning frameworks like JAX, TensorFlow, or PyTorch is essential.

Experience with either large-scale data processing frameworks (e.g., Apache Beam, Spark) or distributed training infrastructure.

Demonstrable expertise in Geospatial AI (GeoAI) and Earth Observation (EO) data modalities, specifically working with vision models and satellite imagery (multi/hyper-spectral, SAR, or LiDAR).

Experience processing and analyzing Earth Observation data for natural environments (e.g., LCLU mapping, change detection, vegetation dynamics).

A proven track record of publications in top-tier conferences and/or journals.

Exceptional expertise in developing and applying multi-modal, multi-task machine learning architectures for remote sensing applications.

Deep domain experience in natural environments, specifically working on geospatial problems such as land cover/land use (LCLU) mapping, change modeling & detection, and vegetation traits estimation.

Experience developing foundation models, including techniques for self-supervised pretraining or handling label noise (weak supervision).

Proficiency with geospatial data processing tools and libraries (e.g., Earth Engine, GDAL/Rasterio, GIS software, GeoPandas) and large-scale data processing frameworks.

Familiarity with the challenges of data curation, such as geospatial annotation design, validation campaign design, and handling sparse, noisy, or geographically biased ground truth data.

A strong passion for environmental sustainability and using AI to address climate change and biodiversity loss.