Remote Machine Learning Scientist Remote Sensing
2 days ago
Leeds
Grow with Treefera\n We are a first-mile intelligence platform, delivering granular visibility into the point of origin in global ag & soft commodity supply chains - where risk, cost, performance and exposure are set. \n \n\n \n You’ll join a global, cross-functional team that values rigour, curiosity and working close to real-world challenges. Whether your focus is AI, climate, product or operations, you’ll have space to contribute meaningfully and make an impact from day one. \n \n If you’re excited by complex problems and want to help reshape how nature is valued in real-world decision-making, we’d love to hear from you. \n Role Purpose & Responsibilities\n We are hiring a Machine Learning Scientist into the Science Team to contribute to the development of models — from classical statistics and gradient-boosted methods through to deep learning when warranted — that turn satellite, radar, and LiDAR observations into defensible, plot-level intelligence on the world's commodity supply chains. You will work across the full lifecycle — from research and prototyping through to validated, productionised models. \n \n A meaningful fraction of the team's work centres on building lightweight downstream models — classifiers, regressors, similarity-search workflows. Likely focus areas for this role include: \n\n • \n Commodity and plantation mapping by region - palm oil, cocoa, coffee, rubber, soy, timber and similar - in support of EUDR compliance and supply-chain due diligence. \n • \n Forest degradation and biomass / canopy-height estimation from multi-sensor fusion. \n • \n Develop ARR feasibility models that fuse climate, soil, and remote-sensing inputs to estimate site potential, forecast biomass and carbon trajectories, and quantify physical and permanence risk. \nResponsibilities:\n\n • \n Design, train and evaluate ML models - from gradient-boosted methods to CNNs, U-Nets and vision transformers - for commodity and plantation mapping, land-cover classification, change and disturbance detection, and biomass / canopy-height estimation. \n • \n Build embedding-driven workflows on top of EO foundation models - few-shot classifiers, similarity search, downstream regressors. \n • \n Design validation strategies that benchmark outputs against plot inventories and third-party reference datasets, quantify uncertainty, and surface failure modes; produce QA artefacts (maps, plots, model cards, error analyses) that internal teams and clients can trust and defend. \n • \n 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. \n\n Who you are:Must-have requirements:\n\n • \n Degree in a quantitative field: environmental/earth science, computer science, physics, maths, engineering, or similar. \n • \n 3+ years of applied machine-learning experience, including time spent in an industry, product, or startup setting i.e. shipping models. \n • \n Expertise in geospatial Python tooling: rasterio, xarray, geopandas, GDAL, and the STAC ecosystem. \n • \n 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. \n • \n Demonstrable experience with remote sensing data (optical, SAR) and an understanding of the sensor-specific quirks that matter for modelling. \n • \n Comfortable with Git, cloud compute (AWS or similar), and collaborative codebases. \n • \n Clear written and verbal communication: can explain modelling choices, uncertainties, and trade-offs to scientific and non-scientific stakeholders. \n • \n Domain exposure: deforestation, land-use change, biomass / canopy-height estimation, climate risk, or supply-chain transparency. \nDesirable requirements:\n\n • \n 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. \n • \n Multi-modal fusion experience - combining optical (Sentinel-2, Landsat), SAR (Sentinel-1, PALSAR), and/or LiDAR (GEDI, ICESat-2) into unified predictions. \n • \n Time-series modelling for environmental change detection - temporal transformers, sequence models, or self-supervised approaches. \n • \n Comfortable building with AI-assisted development tools as a core part of your workflow. \n • \n Familiarity with Google Earth Engine, Microsoft Planetary Computer, AWS Open Data, or other STAC-based catalogues. \n • \n Experience working in cross functional teams working alongside solutions architects, sales, and engineering. \n\n Who you’ll work with\n 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. \n Interview process & what to expect\n This is a Science Team role with regular touch points with Engineering and Product - the interview process gives you a chance to meet your immediate team and a couple of key partners. \n\n • \n Recruiter screen (30 min) \n • \n Hiring manager (45-60 min) \n • \n Science Team technical interview (45 min) \n • \n Engineering and/or Product interview (30 min) \n • \n Potential in-person problem-solving session with the team - designed to be interactive and a glimpse of what being part of the Science Team will be like! \n\n \n Accessibility: Tell us if you need adjustments, we’ll accommodate. \n \n What you’ll gain at Treefera \n\n • \n Build something that matters - join a high-growth climate-tech company applying AI, satellite data and quantitative modelling to real-world challenges across global supply chains, commodities and carbon. \n • \n Work on complex, meaningful problems - develop systems that balance risk, resilience, compliance and sustainability, giving organisations a genuine information advantage at global scale. \n • \n Collaborate with exceptional people - work alongside scientists, engineers and operators who are leaders in their fields, combining academic rigour with practical, cross-functional product delivery. \n • \n Ship and grow in a high-trust environment - experiment, iterate and take thoughtful risks in a team that values autonomy, creativity and continuous learning. \n • \n Develop your craft - dedicated space and time to grow your skills toward mastery, tackling technically demanding challenges that push the boundaries of applied AI and environmental data. \n • \n Be rewarded for your impact - competitive compensation, equity options, meaningful benefits, and the opportunity to help shape the future of AI-powered risk and environmental intelligence. \n\n \n Diversity, Equity & Inclusion \n Bold solutions come from diverse teams. Please refer to our DEI & EEO commitment below. If you need any accommodation during the application process, we’re here to support you. \n \n Learn more about how we think and build \n Many of our engineers, scientists and product leaders share their thinking publicly. Explore the Treefera blog for technical deep dives, research and product perspectives. \n \n Privacy notice \n By applying to Treefera, you consent to the processing of your personal data in line with our Privacy Notice. \n Treefera is an equal opportunity employer. We believe the diversity of our people is as vital as the diversity of the ecosystems we work to protect, and we are committed to building an inclusive workplace where everyone can thrive. We welcome applicants of all backgrounds irrespective of race, colour, ethnicity, national origin, religion, gender identity or expression, sexual orientation, age, disability, pregnancy, or any other characteristic protected by applicable law. Reasonable accommodations are available upon request.
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