As a Staff Machine Learning Engineer, you will help shape and advance our Recommendations product.
As a Staff Machine Learning Engineer, you will help shape and advance our Recommendations product.
As a Staff Machine Learning Engineer, you will help shape and advance our Recommendations product.
As a Senior Machine Learning Engineer, you will help build our new Retail Media product from the ground up.
About : We are seeking a MLOps Engineer (m/f/d) to build, operate, and improve ML/AI solutions in production.
Wir schreiben dir, sobald eine neue geprüfte Stelle zu Data Science dazukommt. Höchstens eine Nachricht am Tag, und nur, wenn es tatsächlich etwas Neues gibt.
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Warum dieser Job gelistet ist
Eindeutige KI-Rollenbezeichnung im Stellentitel. Prüfverfahren ansehen
: As a Staff Machine Learning Engineer, you will help shape and advance our Recommendations product. You will design, develop, and operate production-grade machine learning systems that create relevant and personalized product experiences for customers and measurable value for the business. You will work in a high-impact product team, working with Data & AI colleagues as well as collaborating closely with product, engineering, and business stakeholders. Your work will cover the full ML lifecycle - from problem framing and data understanding to model development, deployment, monitoring, and continuous improvement. As a Staff Engineer, you will also provide technical leadership for the team, shape the architecture and technical direction of our recommendation systems, and help others make strong engineering and machine learning decisions. This role is suited for someone who combines strong machine learning expertise with solid software engineering practices and enjoys turning ambiguous product opportunities into reliable, scalable systems.
: - Work with colleagues from our Data & AI department and collaborate closely with product managers, engineers, and business stakeholders. - Provide technical leadership for our Recommendations product, helping define the architecture, technical direction, and longer-term evolution of our machine learning systems. - Design, build, and operate machine learning systems for recommendation use cases such as candidate generation, ranking, personalization, product discovery, and recommendation optimization. - Translate ambiguous business and product requirements into scalable ML solutions, balancing model quality, latency, reliability, scalability, and maintainability. - Lead technical design and architectural decisions for complex ML initiatives and help the team navigate trade-offs across modeling, data, infrastructure, and product requirements. - Develop robust ML pipelines for feature engineering, training, evaluation, deployment, monitoring, and continuous model improvement. - Bring models into production using our cloud-based stack and ensure they are reliable, observable, and maintainable over time. - Identify technical risks, gaps, and opportunities across the recommendation stack and drive improvements that increase the effectiveness and scalability of the overall system. - Communicate technical decisions, assumptions, limitations, and uncertainty clearly to product, engineering, and business stakeholders. - Raise the technical bar through design reviews, mentoring, knowledge sharing, and by establishing ML engineering standards and best practices within and beyond the team. Qualifications About you: - You have extensive hands-on experience as a Machine Learning Engineer, ML-focused Software Engineer, or Data Scientist with strong engineering experience. - You have built and operated production-grade machine learning systems, pipelines, or model-based products and have taken technical ownership of complex ML systems. - You have strong experience working with recommender systems, ranking, personalization, or related product discovery systems. - You have demonstrated technical leadership, influencing architecture, engineering practices, and technical direction beyond your own individual contributions. - You are comfortable working with complex data and understand common ML failure modes such as data leakage, feedback loops, distribution shifts, and misleading offline metrics. - You can reason about system-level trade-offs and make pragmatic technical decisions across model quality, latency, reliability, scalability, and maintainability. - You enjoy working close to the business and can explain complex technical topics and trade-offs clearly to technical and non-technical stakeholders. - You take ownership of ambiguous, cross-cutting problems, work proactively, and can drive technical initiatives across team boundaries. - You value collaboration, give and receive feedback openly, and actively help other engineers grow through mentoring and technical guidance.
: - Sports: Stay healthy. Profit from a membership (M) package at Urban Sports Club, so that you can take advantage of a huge variety of sport offers. - Mental Health: Get quick and professional help from psychologists of Likeminded if you feel overwhelmed in private or professional life. Anonymous and free of charge. - Work from Home: If your job does not require you to be present in the office, we can arrange the place you work from individually - even for up to 20 days a year anywhere in the EU. - Mobility: We provide our employees with a fully costed Deutschland Ticket which can be used at any time. Click here to learn more. - Personal development: Grow! We support and encourage your individual development through various in- and external trainings. - And many more :)
About Redcare Pharmacy: As Europe’s No.1 e-pharmacy, Redcare Pharmacy is powered by passionate teams and cutting-edge innovation. We strive to create a healthy, collaborative work environment where every employee feels valued and inspired to contribute to our vision “Until every human has their health”. If you’re seeking a career that offers purpose and aligns with your values, join us and begin your #Redcareer today.
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Zur OriginalanzeigeData Science steht in Anzeigen oft für die Arbeit vor dem Modell: Daten zusammentragen, verstehen, bereinigen, eine Frage so stellen, dass sie beantwortbar wird. Viele dieser Rollen heißen weiterhin Data Scientist, haben in den letzten Jahren aber KI-Aufgaben dazubekommen, etwa ein Sprachmodell für eine Auswertung einzusetzen. Verlangt werden meist SQL, Python und die Fähigkeit, ein Ergebnis einem Fachbereich zu erklären, der keine Statistik studiert hat. Wer lieber Modelle baut als Fragen beantwortet, sucht besser unter Machine Learning.