Machine Learning Scientist, Treefera

• 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

• We are hiring a Machine Learning Scientist into the Science Team to contribute to the development of models — from classical statistics and gradient-boosted methods through to deep learning when warranted — that turn satellite, radar, and LiDAR observations into defensible, plot-level intelligence on the world's commodity supply chains
• You will work across the full lifecycle — from research and prototyping through to validated, productionised models
• A meaningful fraction of the team's work centres on building lightweight downstream models — classifiers, regressors, similarity-search workflows. Likely focus areas for this role include:
• Commodity and plantation mapping by region - palm oil, cocoa, coffee, rubber, soy, timber and similar - in support of EUDR compliance and supply-chain due diligence
• Forest degradation and biomass / canopy-height estimation from multi-sensor fusion
• Develop ARR feasibility models that fuse climate, soil, and remote-sensing inputs to estimate site potential, forecast biomass and carbon trajectories, and quantify physical and permanence risk
• Design, train and evaluate ML models - from gradient-boosted methods to CNNs, U-Nets and vision transformers - for commodity and plantation mapping, land-cover classification, change and disturbance detection, and biomass / canopy-height estimation
• Build embedding-driven workflows on top of EO foundation models - few-shot classifiers, similarity search, downstream regressors
• Design validation strategies that benchmark outputs against plot inventories and third-party reference datasets, quantify uncertainty, and surface failure modes; 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
• 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

• This is a Science Team role with regular touch points with Engineering and Product - the interview process gives you a chance to meet your immediate team and a couple of key partners
• Recruiter screen (30 min)
• Hiring manager (45-60 min)
• Science Team technical interview (45 min)
• Engineering and/or Product interview (30 min)
• Potential in-person problem-solving session with the team - designed to be interactive and a glimpse of what being part of the Science Team will be like!