Machine Learning Engineer - £110k - £130k - Geospatial Tech 4 Good
Role details
Job location
Tech stack
Job description
Do you want to work with a business building AI-native data system that bring clarity and credibility to nature-based assets?
A business tackling complex, real-world environmental challenges, helping organisations make high-impact decisions around risk, resilience and commercial performance?
This is the chance to join as a Machine Learning Engineer working with a climate-tech scale-up applying cutting-edge Machine Learning to satellite data, weather models and environmental signals, reshaping how nature is valued in real-world decision-making.
Joining their AI team, you'll design and deploy models that forecast climate volatility, detect vegetation stress, and generate risk-driven insights from remote sensing and time-series data. You'll work across AI, climate science, geospatial modelling and scalable pipelines, contributing meaningfully from day one.
What you'll be working on:
- Building and evaluating Machine Learning/DL models for satellite, weather and climate data
- Forecasting environmental and risk-related signals (volatility, vegetation stress, land-surface change)
- Developing geospatial and remote-sensing models (Sentinel-1/2, GEDI, optical, radar, LiDAR)
- Creating time-series and forecasting models for environmental change
- Translating business questions into robust modelling problems
- Turning research prototypes into scalable, reproducible AI pipelines
- Communicating assumptions, uncertainty and results clearly
Requirements
- Strong background in Machine Learning, DL and Applied Statistics
- Time-series modelling + backtesting
- Experience with geospatial and climate datasets
- Python stack: PyTorch, scikit-learn, scipy
- Reproducible workflows (Git, AWS/cloud, W&B)
Nice-to-haves:
- Risk modelling, financial time series, portfolio optimisation (great for FinTech/quant backgrounds)
- Climate/weather datasets (CMIP, forecast data)
- Geospatial tools: rasterio, xarray, geopandas, GDAL
- Remote sensing (optical, radar, LiDAR)
- MLOps: CI/CD, containerisation, monitoring
- Startup or fast-paced product environment
Benefits & conditions
The role offers £110k-£130k, a global team environment, and the chance to shape the future of AI-powered environmental and risk intelligence.
