Machine Learning Engineer, Green Fusion
Energy management company
Job no longer available
Energy management company
21-100 employees
Job no longer available
21-100 employees
Company mission
To create a new standard for energy management software and to empower the use of renewable energies.
Role
Who you are
- We know that nobody fits a job description 100%. If you see yourself in most of these points and are passionate about our mission, we’d love to hear from you!
- You bring a strong background in Python and machine learning engineering, with hands-on experience developing, testing, and maintaining models in containerized production environments (e.g., Docker, AWS)
- You are familiar with the full machine-learning lifecycle, from training to deployment and monitoring, and you have experience using MLOps tools such as Prefect, MLflow, or similar platforms
- You have experience in time-series forecasting and nonlinear optimization, and ideally you’ve worked with stochastic model predictive control or probabilistic forecasting techniques
- You are curious about how physical and energy systems work, from heat pumps to power markets, and you recognize the importance of validating algorithms that control real-world hardware
- You enjoy collaborating with cross-functional teams (Energy, Backend, Embedded) and can clearly communicate technical concepts to diverse stakeholders
- Bonus Points: You bring experience with Reinforcement Learning, IoT/Edge deployments, or energy management systems (EMS)—a plus, but not a must
What the job involves
- As an ML Engineer, you’ll support our Sector Coupling team building the next generation of intelligent Energy Management Systems (EMS). You will enable high-accuracy model predictions and optimizations through long-term learning from our data to save energy every single day
- You will design and improve machine learning models for time-series forecasting and nonlinear optimization, taking them from concept to deployment
- By working alongside data scientists and energy engineers, you will 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. You ensure feature engineering for time-series, asset telemetry, and market data is robust. You also 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
- You will collaborate with embedded and platform teams to integrate your work into the GreenBox edge device and backend services
Our take
Founded in 2021, Green Fusion is a Berlin-based cloud platform revolutionising energy management. Designed for the smart control of heat, electricity, and e-mobility, it gives users the power to monitor, control, analyse, and uptimes their systems in real time, from anywhere. By leveraging machine learning, Green Fusion boosts efficiency and offer predicative maintenance to keep systems running at their best.
With backing from top investors like High Rise Ventures and Vireo Ventures, Green Fusion has already gained signficant trust and attention. This funding not only reflects the growing demand for its services but also fuels the company's ambitious plans to further develop its cutting-edge technology and secure its place as a leader in the energy management space.
Kirsty
Company Specialist at Welcome to the Jungle
Company
Funding (last 2 of 3 rounds)
Jan 2025
$14.2m
SERIES A
Feb 2023
$3.2m
SEED
Total funding: $17.7m
Company HQ
Oberhavel, Hohen Neuendorf, Germany
Leadership
Paul Hock
(Co-Founder & CEO)Hosts the podcast 'Among Founders'. Has interned at Get Hold of It and ANDRITZ HYDRO.
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