Machine Learning Scientist – Remote Sensing

Treefera

Machine Learning Scientist transforming satellite data into intelligence for climate-tech solutions. Developing models for environmental change detection and supply chain transparency in global commodity sectors.

Posted 7/10/2026full-timeLondon • 🇬🇧 United KingdomMid-LevelSenior💰 £75,000 - £90,000 per yearWebsite

Tech Stack

Tools & technologies

PythonPyTorchRemote SensingScikit-Learn

About the role

Key responsibilities & impact

Transform satellite, radar, and LiDAR signals into precise intelligence that protects forests and fortifies supply chains.
Develop models ranging from EUDR-compliant plantation mapping, to biomass estimation and forest degradation that accelerate decarbonisation and in turn enable confident, risk-adjusted decisions at global scale.
Design, train and evaluate models — from gradient boosting 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 and downstream regressors.
Design validation strategies that benchmark outputs against plot inventories and third-party reference data, quantify uncertainty and surface failure modes — producing QA artefacts (maps, plots, model cards, error analyses) that internal teams and clients can defend.
Partner with Engineering to take models into scalable, reproducible inference pipelines across millions of plots.
Contribute to a strong research culture across Science, AI and Engineering — reviewed code, shared tooling and active engagement with EO/ML literature.

Requirements

What you’ll need

You take models across the full lifecycle — research, prototyping, validation and productionisation — and you have shipped them in an industry, product or startup setting, not only in research.
You are fluent across the modern Python ML stack — deep learning (CNNs, U-Nets, vision transformers) in PyTorch and classical methods (gradient boosting, random forests) in scikit-learn — and you pick the right approach for the problem.
You work fluently with remote sensing data (optical and SAR) and geospatial Python tooling (rasterio, xarray, geopandas, GDAL and the STAC ecosystem), and you understand the sensor-specific quirks that matter for modelling.
You design validation that benchmarks against reference datasets, quantifies uncertainty and surfaces failure modes — and you can explain modelling choices, uncertainties and trade-offs to scientific and non-scientific stakeholders alike.
You collaborate by default across Science, Engineering and Product, bring domain exposure to deforestation, land-use change, biomass or supply-chain transparency, and have a genuine appetite for AI-assisted development workflows.

Benefits

Comp & perks

Unlimited leave
Medical insurance (including optical & dental)
Pension scheme
Group life insurance
Group income protection
Cycle to Work scheme
Company Apple MacBook
Flexible working hours
Snacks and drinks in the office
Monthly team socials

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills & Tools

Model DevelopmentGradient BoostingChange DetectionBiomass EstimationLand-Cover Classification

Soft Skills

CollaborationCommunicationProblem-Solving