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25 дней назад

Staff ML Engineer (Product Recommendations)

Формат работы
onsite
Тип работы
fulltime
Грейд
lead
Английский
b2
Страна
Germany
Вакансия из списка Hirify.GlobalВакансия из Hirify Global, списка международных tech-компаний
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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

hirify.global 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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