Machine Learning Engineer - £110k – £130k – Geospatial Tech 4 Good
Machine Learning | Deep Learning | Time Series | Climate | Remote Sensing | PyTorch | scikit‑learn | Geospatial | AWS | MLOps | Python | Risk Modelling | FinTech |
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
The must‑haves:
• 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
The role offers £110k–£130k, a global team environment, and the chance to shape the future of AI‑powered environmental and risk intelligence.
If it ticks those boxes, don’t hang about message me: (url removed)
Machine Learning | Deep Learning | Time Series | Climate | Remote Sensing | PyTorch | scikit‑learn | Geospatial | AWS | MLOps | Python | Risk Modelling | FinTech