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, deploying, and operating production-grade machine learning systems for candidate generation, ranking, personalization, product discovery, and recommendation optimization with an accent on scalable architecture, model quality, reliability, and maintainability. Focus on leading technical design, building end-to-end ML pipelines, addressing data leakage and feedback loops, and driving continuous improvement across the recommendation stack.
Location: Berlin, Germany. If office presence is not required for the role, the workplace can be arranged individually, including up to 20 work-from-home days per year anywhere in the EU.
Company
is a European e-pharmacy focused on providing digital health and pharmacy services.
What you will do
- Provide technical leadership for the Recommendations product and define the architecture and long-term technical direction of its ML systems.
- 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.
- Lead technical design and architectural decisions across modeling, data, infrastructure, and product requirements.
- Develop ML pipelines for feature engineering, training, evaluation, deployment, monitoring, and continuous model improvement.
- Mentor engineers, lead design reviews, communicate technical trade-offs, and establish ML engineering standards.
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 such as data leakage, feedback loops, distribution shifts, and misleading offline metrics.
- Ability to make pragmatic system-level decisions and explain complex technical topics to technical and non-technical stakeholders.
Culture & Benefits
- Collaborative work with Data & AI colleagues, product managers, engineers, and business stakeholders.
- Individual work-from-home arrangements where the role does not require office presence.
- Up to 20 work-from-home days per year anywhere in the EU.
- Urban Sports Club membership and anonymous, free mental-health support.
- Deutschland Ticket and support for internal and external professional training.
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