Company Overview
Axel Springer is Europe's leading digital publisher and a global media and technology company headquartered in Berlin. With renowned brands such as BILD, POLITICO Germany, WELT, and BUSINESS INSIDER Germany, the company reaches millions of users worldwide. Axel Springer combines the reach of an established industry leader with the agility of a startup, constantly driving innovation to transform journalism for the digital age.
The corporate strategy places AI at the center: "Digital is the new print. AI is the new digital." This vision reflects the belief that the fusion of artificial intelligence and human creativity will shape the future of media. The National Media & Tech division is the central tech hub that ensures the company's journalism is supported by state-of-the-art technology, positioning the brands to thrive in the digital age.
Responsibilities
- Architect and build scalable recommender systems end-to-end, from feature engineering and modeling to reliable production serving
- Implement and integrate modern AI and LLM-based capabilities into scalable production systems
- Write clean, maintainable, and testable production-quality code with a strong focus on reliability and long-term maintainability
- Take full ownership of ML systems in production, including deployment, monitoring, performance optimisation, and system resilience
- Enable controlled experimentation and continuous optimisation of recommender systems in production environments
- Proactively experiment with new approaches, tools, and architectures to continuously improve recommender performance and system design
- Collaborate closely with data scientists, software engineers, data engineers, and product managers to integrate ML solutions into scalable, production-ready system architectures
- Continuously improve engineering standards, tooling, experimentation practices, and system robustness
Requirements
- Several years of hands-on experience operating machine learning systems in production at scale
- Strong software engineering fundamentals, including system design, clean architecture, testing strategies, CI/CD, and code reviews
- Solid data science foundation in recommender systems
- Proficiency in Python and working knowledge of backend languages such as Go or Java, with experience building and operating ML systems in distributed, cloud-based environments (e.g., Spark/PySpark, AWS)
- Practical experience integrating modern AI systems such as LLMs into real-world applications
- Experience designing observable, resilient, and scalable ML systems (monitoring, logging, alerting, performance tracking)
- Strong background in experimentation and controlled rollouts in production environments
- A pragmatic, solution-oriented mindset with a strong builder mentality and ownership attitude
- Ability to operate confidently as a senior engineer within cross-functional product and engineering teams
- Excellent communication skills in English; German skills are an advantage
Benefits
- Trainings and learning lunches, tech conferences, a budget for workshops, and more to expand your knowledge and skills
- Flexible working hours that support a healthy work-life balance
- Free food in the canteens, including a breakfast snack and a free lunch
- 30 days vacation plus 10 days working from abroad
- Free choice of top-notch office equipment (also for your remote work space and private usage), up-to-date hardware, software, and modern office spaces that provide maximum flexibility
- Collaboration through direct exchange, with an emphasis on 80% office presence and 20% mobile working