Senior ML Scientist: Geospatial & Climate AI

NLP PEOPLE; Cirencester; Hybrid; GBP 65,000 - 75,000; Full time

Benefits offered by this job

Salary to £75k

Equity

Hybrid work

Job summary

Client Server in the UK is seeking a PhD-level Machine Learning Researcher to apply research to environmental challenges. You’ll lead the intersection of physics and AI and work with satellite imagery and geospatial data to deliver trusted insights.

You’ll design production-ready ML models, collaborate with the Science Lead, and mentor others while shaping the research agenda from day one. Hybrid work in the UK is available.

Qualifications

• PhD or equivalent in ML, Physics, Geophysics, Applied Mathematics, Computational Science or related field.
• Strong research background at the intersection of machine learning and physical modelling.
• Experience developing ML models for remote sensing or geospatial data.
• Strong Python software engineering skills for reproducible, production-quality research.
• Experience with uncertainty quantification and explainable AI.
• Ability to lead technically, work autonomously and mentor others.
• Knowledge of cryosphere/climate topics is advantageous but not essential.

Responsibilities

• Define the company’s scientific direction at the intersection of physics and AI.
• Design and deploy production-ready ML models for geospatial data.
• Collaborate with the Science Lead and mentor others in the team.
• Shape the research agenda and influence future scientific hires.
• Validate models against real-world observations and ensure explainability.

Skills

Python programming

ML research

Geospatial data

Leadership

Explainable AI

Education

PhD or equivalent

Tools

Xarray

Dask

Zarr

STAC

Job description

Client Server in the UK is seeking a PhD-level Machine Learning Researcher to apply research to environmental challenges. You’ll lead the intersection of physics and AI and work with satellite imagery and geospatial data to deliver trusted insights.

You’ll design production-ready ML models, collaborate with the Science Lead, and mentor others while shaping the research agenda from day one. Hybrid work in the UK is available.