At a Glance
- Tasks: Develop machine learning models to analyse satellite and remote sensing data for global supply chains.
- Company: Join Treefera, a pioneering platform transforming agri-tech with innovative intelligence solutions.
- Benefits: Flexible remote work, competitive salary, and opportunities for professional growth.
- Other info: Collaborative team culture focused on research and innovation in AI and environmental science.
- Why this job: Make a real-world impact by solving complex environmental challenges with cutting-edge technology.
- Qualifications: Degree in a quantitative field and 3+ years of applied machine learning experience required.
The predicted salary is between 60000 - 80000 £ per year.
We are a first-mile intelligence platform, delivering granular visibility into the point of origin in global ag & soft commodity supply chains - where risk, cost, performance and exposure are set.
You'll join a global, cross functional team that values rigour, curiosity and working close to real world challenges.
Whether your focus is AI, climate, product or operations, you'll have space to contribute meaningfully and make an impact from day one.
If you're excited by complex problems and want to help reshape how nature is valued in real world decision making, we'd love to hear from you.
Role Purpose & Responsibilities
We are hiring a Machine Learning Scientist 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 Li DAR 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.
- Primary Focus Areas
- 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.
Responsibilities
- 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.
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 (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 Py Torch, and 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., Alpha Earth Foundations, Clay, etc.) and 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 Li DAR (GEDI, ICESat 2) into unified predictions.
- Time series modelling for environmental change detection - temporal transformers, sequence models, or self supervised approaches.
- Company
- Treefera
