Machine Learning Engineer

• Collaborate on the creation and maintenance of scalable data pipelines for processing and analyzing large datasets
• Contribute to the automation and deployment of machine learning solutions using modern tools and frameworks
• Work closely with the software development team to ensure high-quality, efficient, and reproducible code

You'll fit in with us if you're skilled in software engineering or data science and passionate about accelerating the energy transition. Your background can be in computer science, physics, meteorology, energy engineering or related subjects. Your ability for thorough analysis, brilliant deduction, and meticulous implementation matters more than your major.

We create distributed applications in Java and Python, using state of the art software infrastructure. You will lead the way in further advancing our forecast accuracy, based on a keen understanding of both data and algorithms., * Master's degree (or equivalent) in computer science or data science, ideally combined with meteorology or physics

• Deep understanding of machine learning algorithms
• Knowledge of Python and Java
• Experience with professional software development process and tools like CI/CD, Gitlab is a plus
• You have a keen eye for details and high quality standards
• Excellent English language skills

• An exciting job that helps build the renewable energy future
• A highly motivated DevOps team of data scientists, developers and support engineers
• Flexible working at our attractive workspace in the Science Park Kassel and in the home office, including the possibility of fully remote work
• An opportunity to make your mark with your own creative ideas and contributions
• Potential for personal development through work with current technologies, individual training and participation in international research projects
• Attractive fixed salary
• Permanent employment contract

Since 2011 we have been using artificial intelligence to drive forward the energy transition. We are a leading provider of power generation forecasts for renewable energy, enabling the full utilization of wind and solar power in 30 countries around the world. Going forward, we will make machine learning methods accessible for even broader use in the energy industry and smart energy applications.