Machine Learning Engineer

Digitalization and energy transition in one sentence?
That's what we do at Green Fusion

Our software holistically optimizes energy systems in the real estate sector,
helping to combat climate change through digitalization and automation. We reduce emissions and energy consumption, actively advancing the energy transition.

Tasks

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.

Requirements

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.

Benefits

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

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