Grow with Treefera We are a first-mile intelligence platform, delivering granular visibility into the point of origin in global ag produce QA artefacts (maps, plots, model cards, error analyses) that internal teams and clients can trust and defend. Partner with Engineering to take models into scalable, reproducible inference pipelines across millions of plots, and contribute to a strong research culture across Science, AI and Engineering - reviewed code, shared tooling, and active engagement with EO/ML literature. Who you are: Must-have requirements: Degree in a quantitative field: environmental/earth science, computer science, physics, maths, engineering, or similar. 3+ years of applied machine-learning experience, including time spent in an industry, product, or startup setting i.e. shipping models. Expertise in geospatial Python tooling : rasterio, xarray, geopandas, GDAL, and the STAC ecosystem. Hands-on experience training, evaluating, and debugging ML models across the modern Python stack - deep learning (CNNs, U-Nets, vision transformers) using PyTorch, as well as classical methods (gradient boosting, random forests) with scikit-learn. Demonstrable experience with remote sensing data (optical, SAR) and an understanding of the sensor-specific quirks that matter for modelling. Comfortable with Git, cloud compute (AWS or similar), and collaborative codebases. Clear written and verbal communication: can explain modelling choices, uncertainties, and trade-offs to scientific and non-scientific stakeholders. Domain exposure : deforestation, land-use change, biomass / canopy-height estimation, climate risk, or supply-chain transparency. Desirable requirements: Experience using EO foundation models as a downstream substrate - building lightweight classifiers, regressors, or similarity-search workflows on top of frozen embeddings (e.g., AlphaEarth Foundations, Clay, etc). Comfortable fine-tuning or pretraining where the case justifies it. Multi-modal fusion experience - combining optical (Sentinel-2, Landsat), SAR (Sentinel-1, PALSAR), and/or LiDAR (GEDI, ICESat-2) into unified predictions. Time-series modelling for environmental change detection - temporal transformers, sequence models, or self-supervised approaches. Comfortable building with AI-assisted development tools as a core part of your workflow. Familiarity with Google Earth Engine, Microsoft Planetary Computer, AWS Open Data, or other STAC-based catalogues. Experience working in cross functional teams working alongside solutions architects, sales, and engineering. Who you’ll work with You’ll report to the Science Team Lead and partner day-to-day with the wider Science Team while working closely with Engineering and Product teams. Interview process
