About the role

Machine Learning Engineer at Green Fusion developing intelligent Energy Management Systems to combat climate change. Collaborating with cross-functional teams for model optimization and deployment.

Responsibilities

Support our Sector Coupling team building the next generation of intelligent Energy Management Systems (EMS).
Enable high-accuracy model predictions and optimizations through long-term learning from our data to save energy every single day.
Design and improve machine learning models for time-series forecasting and nonlinear optimization, taking them from concept to deployment.
Bring forecasting and optimization models into our EMS production environment (Cloud and Edge).
Maintain and improve ML pipelines (using tools like Prefect and MLFlow) to support the full model lifecycle—from experiment tracking to training and validation.
Act as the guardian of our data, ensuring feature engineering for time-series, asset telemetry, and market data is robust.
Lead the monitoring of model quality, handling concept drift and performance evaluation.
Lead the development of digital twins and simulation environments to safely test how our EMS interacts with components before they touch real hardware.
Collaborate with embedded and platform teams to integrate your work into the GreenBox edge device and backend services.

Strong background in Python and machine learning engineering.
Hands-on experience developing, testing, and maintaining models in containerized production environments (e.g., Docker, AWS).
Familiarity with the full machine-learning lifecycle, from training to deployment and monitoring.
Experience using MLOps tools such as Prefect, MLflow, or similar platforms.
Experience in time-series forecasting and nonlinear optimization.
Ideally experienced with stochastic model predictive control or probabilistic forecasting techniques.
Curious about how physical and energy systems work, from heat pumps to power markets.
Enjoy collaborating with cross-functional teams (Energy, Backend, Embedded) and can clearly communicate technical concepts to diverse stakeholders.
Bonus Points: Experience with Reinforcement Learning, IoT/Edge deployments, or energy management systems (EMS).

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.