At a Glance
- Tasks: Develop and deploy machine learning models for remote sensing applications.
- Company: Join Treefera, a pioneering first-mile intelligence platform in agri-tech.
- Benefits: Flexible remote work, competitive salary, and opportunities for professional growth.
- Other info: Collaborative environment with a strong focus on research and innovation.
- Why this job: Make a real impact on global agriculture with cutting-edge technology.
- Qualifications: Degree in a quantitative field and 3+ years of machine learning experience.
The predicted salary is between 70000 - 90000 £ per year.
Grow with Treefera. We are a first-mile intelligence platform, delivering granular visibility into the point of origin in global ag produce QA artefacts (maps, plots, model cards, error analyses) that internal teams and clients can trust and defend. Partner with Engineering to take models into scalable, reproducible inference pipelines across millions of plots, and contribute to a strong research culture across Science, AI and Engineering - reviewed code, shared tooling, and active engagement with EO/ML literature.
Who you are:
- Must-have requirements:
- Degree in a quantitative field: environmental/earth science, computer science, physics, maths, engineering, or similar.
- 3+ years of applied machine-learning experience, including time spent in an industry, product, or startup setting i.e. shipping models.
- Expertise in geospatial Python tooling: rasterio, xarray, geopandas, GDAL, and the STAC ecosystem.
- Hands-on experience training, evaluating, and debugging ML models across the modern Python stack - deep learning (CNNs, U-Nets, vision transformers) using PyTorch, as well as classical methods (gradient boosting, random forests) with scikit-learn.
- Demonstrable experience with remote sensing data (optical, SAR) and an understanding of the sensor-specific quirks that matter for modelling.
- Comfortable with Git, cloud compute (AWS or similar), and collaborative codebases.
- Clear written and verbal communication: can explain modelling choices, uncertainties, and trade-offs to scientific and non-scientific stakeholders.
- Domain exposure: deforestation, land-use change, biomass/canopy-height estimation, climate risk, or supply-chain transparency.
- Desirable requirements:
- Experience using EO foundation models as a downstream substrate - building lightweight classifiers, regressors, or similarity-search workflows on top of frozen embeddings (e.g., AlphaEarth Foundations, Clay, etc).
- Comfortable fine-tuning or pretraining where the case justifies it.
- Multi-modal fusion experience - combining optical (Sentinel-2, Landsat), SAR (Sentinel-1, PALSAR), and/or LiDAR (GEDI, ICESat-2) into unified predictions.
- Time-series modelling for environmental change detection - temporal transformers, sequence models, or self-supervised approaches.
- Comfortable building with AI-assisted development tools as a core part of your workflow.
- Familiarity with Google Earth Engine, Microsoft Planetary Computer, AWS Open Data, or other STAC-based catalogues.
- Experience working in cross-functional teams working alongside solutions architects, sales, and engineering.
Who you’ll work with:
You’ll report to the Science Team Lead and partner day-to-day with the wider Science Team while working closely with Engineering and Product teams.
Interview process
Remote Machine Learning Scientist Remote Sensing in Peterborough employer: Treefera
Treefera is an exceptional employer that empowers its employees to tackle complex, meaningful challenges in the climate-tech sector. With a strong focus on collaboration and innovation, team members enjoy a high-trust environment that fosters autonomy and continuous learning, alongside competitive compensation and equity options. Located remotely, Treefera offers the unique advantage of working with a diverse, cross-functional team dedicated to reshaping how nature is valued in global supply chains.
StudySmarter Expert Advice🤫
We think this is how you could land Remote Machine Learning Scientist Remote Sensing in Peterborough
✨Tip Number 1
Network like a pro! Reach out to folks in the industry, especially those at Treefera. A friendly chat can open doors and give you insights that a job description just can't.
✨Tip Number 2
Show off your skills! Prepare a portfolio showcasing your machine learning projects, especially those involving geospatial data. This will help you stand out and demonstrate your hands-on experience.
✨Tip Number 3
Practice makes perfect! Get ready for technical interviews by brushing up on your Python skills and ML concepts. Use platforms like StudySmarter to review key topics and tackle mock interviews.
✨Tip Number 4
Apply through our website! It’s the best way to ensure your application gets seen. Plus, it shows you're genuinely interested in joining the Treefera team.
We think you need these skills to ace Remote Machine Learning Scientist Remote Sensing in Peterborough
Some tips for your application 🫡
Show Off Your Skills:Make sure to highlight your experience with machine learning and geospatial Python tools. We want to see how you've applied your skills in real-world scenarios, so don't hold back on sharing specific projects or achievements!
Tailor Your Application:Take a moment to customise your application for the role. Use keywords from the job description to demonstrate that you understand what we're looking for. This shows us you're genuinely interested and have done your homework!
Be Clear and Concise:When writing your application, clarity is key! We appreciate straightforward communication, so make sure your points are easy to follow. Avoid jargon unless it's relevant, and keep it professional yet approachable.
Apply Through Our Website:We encourage you to apply directly through our website. It’s the best way for us to receive your application and ensures you’re considered for the role. Plus, it makes the whole process smoother for everyone involved!
How to prepare for a job interview at Treefera
✨Know Your Stuff
Make sure you brush up on your machine learning concepts, especially those related to remote sensing and geospatial data. Be ready to discuss your experience with Python libraries like rasterio and PyTorch, as well as any specific projects you've worked on that relate to the job description.
✨Showcase Your Collaboration Skills
Since you'll be working closely with Engineering and Product teams, highlight your experience in cross-functional teams. Prepare examples of how you've effectively communicated complex ideas to both technical and non-technical stakeholders.
✨Prepare for Technical Questions
Expect to dive deep into your technical expertise during the interview. Brush up on your knowledge of model training, evaluation, and debugging. Be ready to explain your modelling choices and the trade-offs involved, especially in relation to environmental data.
✨Engage with Current Research
Stay updated on the latest trends and literature in EO/ML. Bring up recent papers or advancements that excite you during the interview. This shows your passion for the field and your commitment to contributing to a strong research culture.
