Machine Learning Engineer
vor 4 Wochen
At Green Fusion, we combine digitalization and energy transition to create a sustainable future
Our software optimizes heating systems in the real estate sector, tackling climate change through innovative digital solutions and automation. By reducing emissions and energy consumption, we are actively driving the energy transition forward.
As a Machine Learning Engineer (working student), you will support our Energy Optimization Team by conducting research and developing prototypes for machine learning and reinforcement learning-based energy optimization algorithms.
Tasks- ML Research: Explore the latest literature on state-of-the-art machine learning concepts related to time-series prediction, digital twin modeling, and energy optimization algorithms.
- Time Series Forecasting: Collaborate with our ML team to develop prototypes for forecasting heating demand and PV production using state-of-the art ML models, and evaluate different approaches.
- Digital Twin Modeling: Assist in creating state-of-the-art data-driven models for heating systems.
- Reinforcement Learning (RL) Algorithms: Support the training of RL agents to optimize the operational control of heat pumps for optimal efficiency.
- Algorithm Development: Contribute to the design and implementation of innovative algorithms that improve energy management and efficiency.
- Collaboration: Work closely with energy engineers, ML engineers, and data scientists to develop innovative features for our smart home energy management system.
- Clear Communication: Effectively present findings and insights in a concise and understandable manner, ensuring clarity for all stakeholders.
- You are currently enrolled as a student at a university
- You are able to work at least one day in our office in Berlin
- You have proven experience in at least one personal machine learning project, ideally in areas like time series forecasting, digital twin modeling, or reinforcement learning.
- You bring strong skills in statistics, machine learning, data modeling, data processing, and data cleaning, with particular expertise in working with time series data.
- You are proficient in Python, Git, Pandas, SciPy, Scikit-Learn, and PyTorch/TensorFlow. Your programming style is clean, well-structured, and maintainable, making it easy for team members to understand and collaborate.
- Proactive and eager to learn, you have a genuine interest in technical topics and enjoy sharing knowledge with others.
- You communicate complex insights clearly to a range of stakeholders and foster a collaborative team environment.
- You are fluent in English and have a basic understanding of German.
An opportunity to expand your skill set in machine learning and reinforcement learning by training models with real production potential
Flexible working hour models, home office, and remote work.
Ongoing training opportunities – whether through job challenges, our open feedback culture, or sponsored training programs, there are always opportunities to learn and grow.
Employee benefits such as Urban Sports Club or Become1.
Direct impact through your job – with us, you can actively contribute to the energy transition and fight against climate change every day.
We value our team – that's why regular team events are very important to us.
The best team that Berlin has to offer – and maybe even beyond. Don’t believe it? Then find out for yourself and apply now
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While we are still considered pioneers today, we can soon dominate the market with you First in the DACH region, then throughout Europe.
You can expect a motivated, open-minded, and dynamic environment that is passionate and ambitious about actively shaping the energy transition – a goal that can only be achieved together
We look forward to your application – Fernanda will get in touch with you
Ein nachhaltiges Energiemanagement in der Wohnungswirtschaft ist nicht nur bald gesetzlich verpflichtend, sondern darüber hinaus ein aktiver Schritt gegen den Klimawandel, hin zu einer grüneren Zukunft für uns alle.
Durch eine signifikante Reduzierung des Heizenergieverbrauchs in Immobilien können nicht nur Ressourcen und Kosten gespart, sondern auch die CO2-Emissionen deutlich gesenkt werden.
Und mit Hilfe der Digitalisierung der Anlagen können diese effizient betrieben werden - automatisiert, vorausschauend und sich selbst optimierend. Probleme können zudem früher erkannt und direkt behoben werden.
Klingt zu schön um wahr zu sein? Nein, mit Green Fusion wird genau das möglich Wir digitalisieren nicht nur Heizungskeller, wir automatisieren und optimieren den gesamten Betrieb und sorgen für die reibungslose Transformation hin zu regenerativen Systemen. Für maximale Effizienz und Nachhaltigkeit in der Wohnungswirtschaft.
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