Working Student Machine Learning Engineer

Do you have experience in Python?, * You are enrolled in CS or a related program at a university in Bavaria

• You are available 20 hours per week and can work with us onsite in Munich
• You have practical experience with timeseries data and forecasting problems
• Tools like Pandas, scikit-learn, Pytorch are second nature to you
• You are passionate about your work and love to collaborate with others
• You enjoy writing clean, maintainable code and care about performance and usability
• You can communicate clearly in English (C1 level)

• Work closely with experienced engineers who care about design, scalability, and quality
• Apply state of the art ML research
• Learn how to build production ready ML models
• A great office at the Sendlinger Tor
• Awesome team events, good coffee, and a culture that prizes clean code, feedback, and collaboration
• Strong long-term perspective: Many of our full-time team members started as working students

Ready to build Delicious Data with us? We're excited to hear from you.     If you require alternative methods of application or screening, you must approach the employer directly to request this as Indeed is not responsible for the employer's application process.

We build AI that helps bakeries and food businesses plan perfectly, so they waste less, earn more, and serve with pride. You'll join a small, focused engineering team in Munich that values clear thinking, craftsmanship, and real-world impact. Our developers come from diverse backgrounds, from research and data science to large-scale production systems, and share a simple principle: build things that work beautifully. As a working student, you'll develop and ship features end to end, learn from experienced engineers, and see your work make a visible difference. Tasks * Real forecasting at real scale: Work with time series data from thousands of stores, thousands of products, and years of history totalling over 1 billion records. * Proprietary ML systems, not off-the-shelf magic: Help develop, improve, and extend our in-house forecasting pipeline that blends deep learning, random forests, and domain knowledge. * From research to production: You'll help bring models into production, monitor their performance, analyze failure modes, and iterate based on real customer behavior. * Food waste, quantified: Your work directly reduces overproduction and saves tons of food every day, with measurable impact beyond the hype. * Learn by doing, together: Exchange code reviews with senior ML engineers, discuss modeling decisions, and learn how production ML actually works in a fast-moving startup.