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Treefera is a first-mile intelligence platform delivering granular visibility into global agricultural and soft commodity supply chains using AI, satellite data, and quantitative modelling. As a Senior AI/ML Engineer, you will own the models and data pipelines that transform weather and satellite data into the risk and market signals customers rely on—answering questions like where agricultural stress is building and how confident we can be about it.
You will take problems end-to-end: from ambiguous questions to validated models to production pipelines that run repeatedly. Your work spans building deep learning and statistical models for time series and spatial data, designing data pipelines from ingestion through modelling and evaluation to customer delivery, and extending the weather platform with new forecast products and capabilities. You'll work with satellite data across optical and radar missions to build vegetation stress signals, landcover classifications, and land-surface conditions. You'll also take research from prototype to production, building the infrastructure it runs on, designing failure modes, and turning one-off work into orchestrated, reproducible data deliveries.
Success milestones: within 30 days, you'll have the weather and earth observation pipelines running locally and have interrogated the current signal-production system; by 60 days, you'll have delivered your first improvement (new index, better evaluation, or forecast product); by 90 days, that work runs in production with external stakeholders relying on it; by six months, you own a signal domain end-to-end and are the go-to person when numbers need validation; within a year, you'll have shipped materially better forecasting capability with evaluation evidence to prove it.
You'll report to Tommy Lees, work inside the AI pod, and partner closely with the Science team on methods, Engineering on platform infrastructure, and Product and Market Intelligence on customer signal requirements. The role emphasizes rigour, curiosity, working close to real-world challenges, and contributing meaningfully from day one.
Requirements:
- Deep experience building deep learning and statistical models for time series or spatial data, with real projects you can walk through in detail, including failures
- Fluency in the Python scientific stack (PyTorch, scikit-learn, scipy, xarray)
- Strong practices in reproducible work: version control, experiment tracking, orchestration, cloud infrastructure
- Ability to interrogate data before modelling, state assumptions clearly, and communicate uncertainty straightforwardly to stakeholders
- Ability to take problems end-to-end and explain every decision in shipped code
- Desirable: experience with weather and climate data (reanalysis products, numerical weather forecasts, weather station records, forecast verification); experience with remote sensing datasets; exposure to risk modelling, financial time series, commodity markets, backtesting, and tradable signal generation
