ml engineer for product recommendations

Redcare Pharmacy is Europe’s No.1 e-pharmacy, providing online pharmacy services and personalized product experiences through technology and innovation.

• Collaborate with Data & AI colleagues, product managers, engineers, and business stakeholders;
• Provide technical leadership for the Recommendations product and define the architecture, technical direction, and long-term evolution of machine learning systems;
• Design, build, and operate machine learning systems for candidate generation, ranking, personalization, product discovery, and recommendation optimization;
• Translate ambiguous business and product requirements into scalable ML solutions while balancing model quality, latency, reliability, scalability, and maintainability;
• Lead technical design and architectural decisions for complex ML initiatives and navigate trade-offs across modeling, data, infrastructure, and product requirements;
• Develop ML pipelines for feature engineering, training, evaluation, deployment, monitoring, and continuous model improvement;
• Bring models into production using the cloud-based stack and ensure reliability, observability, and maintainability;
• Identify technical risks, gaps, and opportunities across the recommendation stack and drive improvements to system effectiveness and scalability;
• Communicate technical decisions, assumptions, limitations, and uncertainty to product, engineering, and business stakeholders;
• Raise engineering standards through design reviews, mentoring, knowledge sharing, and ML best practices.

• Extensive hands-on experience as a Machine Learning Engineer, ML-focused Software Engineer, or Data Scientist with strong engineering experience;
• Experience building and operating production-grade machine learning systems, pipelines, or model-based products;
• Technical ownership of complex ML systems;
• Strong experience with recommender systems, ranking, personalization, or related product discovery systems;
• Demonstrated technical leadership influencing architecture, engineering practices, and technical direction beyond individual contributions;
• Ability to work with complex data and understand ML failure modes such as data leakage, feedback loops, distribution shifts, and misleading offline metrics;
• Ability to reason about system-level trade-offs and make pragmatic decisions across model quality, latency, reliability, scalability, and maintainability;
• Ability to explain complex technical topics and trade-offs clearly to technical and non-technical stakeholders;
• Ownership of ambiguous, cross-cutting problems and ability to drive technical initiatives across team boundaries;
• Collaborative approach, openness to feedback, and ability to mentor and guide other engineers.

• Sports membership package at Urban Sports Club;
• Anonymous and free psychological support from Likeminded;
• Up to 20 work-from-home days per year anywhere in the EU;
• Fully funded Deutschland Ticket;
• In-house and external training opportunities.