25 дней назад
Staff ML Engineer (Product Recommendations)
Мэтч & Сопровод
Для мэтча с этой вакансией нужен Plus
Описание вакансии
Текст:
TL;DR
Staff ML Engineer (Product Recommendations) (Machine Learning/Recommender Systems): Designing, developing, and operating production-grade machine learning systems for personalized product experiences with an accent on recommendation architecture, ranking, personalization, and full-lifecycle ML operations. Focus on leading technical decisions, building reliable ML pipelines, and balancing model quality, latency, scalability, and maintainability in complex recommendation systems.
Location: Munich, Germany; full-time. Work-from-home arrangements may be available where office presence is not required, including up to 20 days per year from anywhere in the EU.
Company
is a European e-pharmacy focused on digital health products and services.
What you will do
- Provide technical leadership for the Recommendations product and define the architecture and long-term technical direction.
- Design, build, and operate machine learning systems for candidate generation, ranking, personalization, product discovery, and recommendation optimization.
- Translate ambiguous product and business requirements into scalable solutions while balancing model quality, latency, reliability, scalability, and maintainability.
- 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 reliable, observable, and maintainable systems.
- Raise engineering standards through design reviews, mentoring, knowledge sharing, and technical guidance.
Requirements
- 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 ML systems, pipelines, or model-based products.
- Strong experience with recommender systems, ranking, personalization, or product discovery systems.
- Demonstrated technical leadership across architecture, engineering practices, and technical direction.
- Understanding of ML failure modes including data leakage, feedback loops, distribution shifts, and misleading offline metrics.
- Ability to make pragmatic system-level decisions and communicate complex technical trade-offs to technical and non-technical stakeholders.
Culture & Benefits
- Collaborative work with Data & AI, product, engineering, and business stakeholders.
- Urban Sports Club membership package.
- Anonymous, free mental health support from Likeminded.
- Individual work-from-home arrangements where applicable, including up to 20 EU-based days per year.
- Fully funded Deutschland Ticket.
- Support for individual development through internal and external training.
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