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- Tasks: Build AI models to tackle environmental challenges using satellite and climate data.
- Company: Join a climate-tech scale-up making a real impact on nature-based assets.
- Benefits: Competitive salary, remote work, and opportunities for professional growth.
- Other info: Dynamic role with the chance to contribute from day one.
- Why this job: Be part of a team reshaping how we value nature with cutting-edge technology.
- Qualifications: Strong background in Machine Learning, statistics, and experience with geospatial datasets.
The predicted salary is between 110000 - 130000 £ per year.
All candidates should make sure to read the following job description and information carefully before applying.
Do you want to work with a business building AI-native data systems 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
Machine Learning Engineer - Geospatial Tech 4 Good in London employer: Opus Recruitment Solutions Ltd
Contact Detail:
Opus Recruitment Solutions Ltd Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Machine Learning Engineer - Geospatial Tech 4 Good in London
✨Tip Number 1
Network like a pro! Reach out to people in the industry, attend meetups or webinars, and connect with professionals on LinkedIn. We can’t stress enough how personal connections can open doors that applications alone can’t.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your projects, especially those related to machine learning and geospatial tech. We love seeing practical examples of your work, so make sure to highlight any relevant experience you have.
✨Tip Number 3
Prepare for interviews by brushing up on your technical knowledge and problem-solving skills. We recommend practicing common machine learning scenarios and being ready to discuss your thought process. Confidence is key!
✨Tip Number 4
Don’t forget to apply through our website! It’s the best way to ensure your application gets noticed. Plus, we’re always looking for passionate individuals who want to make a difference in climate tech.
We think you need these skills to ace Machine Learning Engineer - Geospatial Tech 4 Good in London
Some tips for your application 🫡
Read the Job Description Thoroughly: Before you dive into your application, make sure to read the job description carefully. We want to see that you understand what we're looking for in a Machine Learning Engineer and how your skills align with our mission.
Tailor Your CV and Cover Letter: Don’t just send a generic CV! Tailor it to highlight your experience with Machine Learning, geospatial data, and any relevant projects. We love seeing how your background fits with what we do at StudySmarter.
Showcase Your Technical Skills: Make sure to emphasise your technical skills, especially in Python, PyTorch, and scikit-learn. We’re keen on candidates who can demonstrate their expertise in building and evaluating models, so don’t hold back!
Apply Through Our Website: We encourage you to apply through our website for a smoother process. It helps us keep track of applications and ensures you get all the updates directly from us. Plus, it shows you're serious about joining our team!
How to prepare for a job interview at Opus Recruitment Solutions Ltd
✨Know Your Tech Inside Out
Make sure you’re well-versed in the specific technologies mentioned in the job description, like PyTorch and scikit-learn. Brush up on your knowledge of geospatial datasets and climate modelling techniques, as you’ll likely be asked to discuss how you’ve applied these in past projects.
✨Prepare Real-World Examples
Think of concrete examples from your previous work that showcase your skills in machine learning and time-series modelling. Be ready to explain how you tackled challenges, particularly those related to environmental data or risk modelling, as this will demonstrate your practical experience.
✨Understand the Business Impact
Familiarise yourself with how machine learning can influence decision-making in climate tech. Be prepared to discuss how your work can help organisations make high-impact decisions around risk and resilience, showing that you understand the bigger picture beyond just the technical aspects.
✨Communicate Clearly and Confidently
During the interview, practice articulating your thoughts clearly, especially when discussing complex topics like uncertainty in models or assumptions in your analyses. This will show that you can communicate effectively with both technical and non-technical stakeholders, which is crucial in a collaborative environment.