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- Tasks: Build AI models to tackle real-world environmental challenges using satellite and climate data.
- Company: Join a climate-tech scale-up focused on nature-based asset clarity.
- Benefits: Competitive salary, global team environment, and impactful work.
- Other info: Fast-paced startup culture with opportunities for meaningful contributions.
- Why this job: Shape the future of AI in environmental decision-making and make a difference.
- Qualifications: Strong background in Machine Learning, geospatial datasets, and risk modelling.
The predicted salary is between 110000 - 130000 £ per year.
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
- 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
- Experience with geospatial and climate datasets
- Risk modelling, financial time series, portfolio optimisation (great for FinTech/quant backgrounds)
- Climate/weather datasets (CMIP, forecast data)
- Remote sensing (optical, radar, LiDAR)
- 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.
Machine Learning Engineer – Geospatial Tech 4 Good employer: Opus Recruitment Solutions
Contact Detail:
Opus Recruitment Solutions Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Machine Learning Engineer – Geospatial Tech 4 Good
✨Tip Number 1
Network like a pro! Reach out to people in the industry, especially those working in climate tech or machine learning. Attend meetups, webinars, or conferences where you can connect with potential employers and showcase your passion for geospatial tech.
✨Tip Number 2
Show off your skills! Create a portfolio that highlights your projects related to machine learning and geospatial data. Use platforms like GitHub to share your code and demonstrate your ability to turn research prototypes into scalable models.
✨Tip Number 3
Prepare for interviews by brushing up on your technical knowledge and problem-solving skills. Be ready to discuss how you've tackled real-world challenges using machine learning and how you can contribute to the company's mission of tackling environmental issues.
✨Tip Number 4
Don’t forget to apply through our website! It’s the best way to ensure your application gets noticed. Plus, we love seeing candidates who are proactive and genuinely interested in joining our mission to make a positive impact.
We think you need these skills to ace Machine Learning Engineer – Geospatial Tech 4 Good
Some tips for your application 🫡
Tailor Your CV: Make sure your CV is tailored to the Machine Learning Engineer role. Highlight your experience with geospatial and climate datasets, as well as any relevant projects that showcase your skills in Machine Learning and applied statistics.
Craft a Compelling Cover Letter: Your cover letter is your chance to shine! Use it to explain why you're passionate about climate tech and how your background aligns with our mission at StudySmarter. Be specific about your experiences and how they relate to the job description.
Showcase Relevant Projects: If you've worked on projects involving satellite data or risk modelling, make sure to include them in your application. We love seeing real-world applications of your skills, so don’t hold back on sharing your achievements!
Apply Through Our Website: We encourage you to apply through our website for the best chance of getting noticed. It helps us keep track of applications and ensures you’re considered for the role. Plus, it’s super easy!
How to prepare for a job interview at Opus Recruitment Solutions
✨Know Your Machine Learning Stuff
Make sure you brush up on your machine learning fundamentals, especially around deep learning and applied statistics. Be ready to discuss specific models you've built or worked with, particularly in relation to satellite data or climate datasets. This will show that you not only understand the theory but can apply it practically.
✨Get Familiar with Geospatial Data
Since this role involves working with geospatial and climate datasets, take some time to review the types of data you'll be dealing with. Understand how remote sensing works and be prepared to talk about any experience you have with optical, radar, or LiDAR data. This knowledge will help you stand out as a candidate who is ready to hit the ground running.
✨Translate Business Needs into Tech Solutions
Practice articulating how you can turn business questions into robust modelling problems. Think of examples from your past work where you successfully translated complex requirements into actionable insights. This will demonstrate your ability to bridge the gap between technical and non-technical stakeholders.
✨Communicate Clearly and Confidently
During the interview, focus on communicating your assumptions, uncertainties, and results clearly. Use simple language to explain complex concepts, as this shows you can make your work accessible to others. Remember, it's not just about what you know, but how well you can share that knowledge.