Machine Learning Engineer – Solar & Energy

This solar company is on a mission to accelerate the energy transition across Europe. They're building the infrastructure and intelligence layer that makes renewable energy work at scale — and ML is at the heart of it. If you want your work to matter, this is the place. As an ML Engineer, you'll build, train, and deploy models that solve real problems: predicting solar generation, optimising energy storage, forecasting demand, and keeping the grid balanced. The data is rich — weather, sensors, market prices, consumption patterns — and the problems are genuinely hard. This isn't big tech ML for ads or engagement metrics. It's applied machine learning for the climate crisis. You'll work with a team that cares deeply about impact, ships fast, and believes that software can change the world. If that sounds like you, keep reading.

Responsibilities

• Build and deploy ML models for solar forecasting, demand prediction, and energy optimisation
• Work with time series data from weather systems, sensors, and smart meters across Europe
• Design and maintain ML pipelines — from data ingestion to training to production inference
• Collaborate with engineers to integrate models into real-time energy management systems
• Continuously improve model performance — experimenting, validating, and iterating
• Help shape the ML roadmap and identify new opportunities to apply AI across the business

Qualifications

• An ML Engineer with 2+ years of experience building and deploying models in production.
• Strong Python skills and hands-on experience with ML frameworks (TensorFlow, PyTorch, scikit-learn).
• Comfortable with time series data and forecasting problems.
• Experience with cloud platforms (AWS, GCP, or Azure) and MLOps practices.
• Curious about energy, climate, or sustainability — or excited to learn.
• A pragmatic builder who cares about shipping things that work, not just impressive prototypes

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