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- Tasks: Design and deploy AI models to tackle real-world environmental challenges.
- Company: Join a climate-tech scale-up making a positive impact on nature-based assets.
- Benefits: Competitive salary, global team environment, and the chance to shape AI-powered solutions.
- Why this job: Make a difference by applying cutting-edge tech to climate and geospatial data.
- Qualifications: Strong background in Machine Learning, Python, and experience with geospatial datasets.
- Other info: Dynamic startup culture with opportunities for growth and innovation.
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, 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.
Machine Learning Engineer - £110k - £130k - Geospatial Tech 4 Good 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 - £110k - £130k - Geospatial Tech 4 Good
✨Tip Number 1
Network like a pro! Reach out to people in the industry, attend meetups or webinars, and connect with professionals on LinkedIn. You never know who might have the inside scoop on job openings or can refer you directly.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your projects, especially those related to machine learning and geospatial tech. This gives potential employers a taste of what you can do and sets you apart from the crowd.
✨Tip Number 3
Prepare for interviews by brushing up on relevant topics like climate data analysis and risk modelling. Practice common interview questions and be ready to discuss your past experiences and how they relate to the role.
✨Tip Number 4
Don’t forget to apply through our website! It’s the best way to ensure your application gets seen. Plus, we love seeing candidates who are proactive about their job search!
We think you need these skills to ace Machine Learning Engineer - £110k - £130k - Geospatial Tech 4 Good
Some tips for your application 🫡
Read the Job Description Thoroughly: Before you start your application, make sure to read the job description carefully. It’s packed with info about what we’re looking for and can help you tailor your application to show us why you’re the perfect fit!
Showcase Your Skills: When writing your application, highlight your experience with Machine Learning, Python, and any relevant geospatial or climate datasets. We want to see how your skills align with what we do, so don’t hold back!
Be Clear and Concise: Keep your application clear and to the point. Use straightforward language to explain your experience and how it relates to the role. We appreciate a well-structured application that makes it easy for us to see your strengths.
Apply Through Our Website: We encourage you to apply directly through our website. It’s the best way to ensure your application gets to us quickly and efficiently. Plus, it shows us you’re keen on 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, scikit-learn, and AWS. Brush up on your knowledge of geospatial datasets and remote sensing techniques, as these will likely come up during technical discussions.
✨Prepare Real-World Examples
Think of concrete examples from your past work where you've successfully built or deployed machine learning models. Be ready to discuss how you translated business questions into modelling problems and the impact your work had on decision-making.
✨Show Your Passion for Climate Tech
This role is all about tackling environmental challenges, so express your enthusiasm for climate tech and sustainability. Share any relevant projects or research you've done that aligns with the company's mission to make high-impact decisions around risk and resilience.
✨Ask Insightful Questions
Prepare thoughtful questions about the company’s approach to AI and how they integrate machine learning with climate science. This shows your genuine interest in the role and helps you assess if the company culture aligns with your values.