Bertelsmann SE & Co. KGaA
Staff Machine Learning Scientist – Personalization (Open to Remote) (United Stat
Rol remoto de Staff Machine Learning Scientist con fit claro de ubicación del candidato.
Publicado15 jul 2026
Países elegibles3 países aceptados
Señal de senioritySenior
Modelo de trabajoRemoto
Ubicaciones aceptadas para candidatos
CanadáMéxicoEstados Unidos
Resumen del rol
Staff Machine Learning Scientist – Personalization (Open to Remote) (United Stat
Requisitos y responsabilidades
Contenido del rol extraído en secciones para revisar más rápido.
Specific responsibilities include:
- Define and drive the technical roadmap for personalization and recommender systems, prioritizing roadmap items to meet business goals and defining short-term vision for the team.
Specific responsibilities include:
- Propose and deliver R&D that directly shapes roadmaps, multiple projects, and long-term deliverables. Models are used over the long term by multiple products and teams.
Specific responsibilities include:
- Design and lead the development of software used by multiple teams, ensuring long-term maintainability, scalability, and adaptability.
Specific responsibilities include:
- Ensure complex, multi-service personalization products meet SLAs and provide correct results over time. Adapt systems to changing business needs and resolve multi-product, multi-team service incidents.
Specific responsibilities include:
- Establish and enforce experimentation best practices, including A/B testing frameworks, offline evaluation methodology, and metrics design across personalization surfaces.
Specific responsibilities include:
- Lead team meetings, ensure the team's progress on the roadmap, and make technical decisions that unblock projects.
Specific responsibilities include:
- Manage stakeholders' expectations with data-driven narratives and communicate effectively with senior leadership to align on strategy and track progress.
Specific responsibilities include:
- Drive organizational efficiency and business impact by implementing new technologies and processes. Foster a collaborative and high-performance team culture.
Specific responsibilities include:
- Mentor senior and mid-level scientists, setting high code quality standards and best practices for the team.
Specific responsibilities include:
- Stay current with advances in recommender systems, LLMs for personalization, and representation learning, bringing relevant advances into production when they deliver measurable improvement.
Basic qualifications:
- PhD in Computer Science, Machine Learning, Engineering, Operations Research, Statistics, or a related quantitative field, OR Master's with 8+ years of applied ML experience.
Basic qualifications:
- Deep expertise in recommender systems, personalization, ranking/retrieval, or computational advertising, with a track record of shipping systems that operate at scale.
Basic qualifications:
- Expert-level Python and deep proficiency with modern ML frameworks (PyTorch or TensorFlow) and recommendation-specific tooling (e.g., NVTabular, Merlin, Triton).
Basic qualifications:
- Strong experience with cloud-based ML infrastructure (AWS, Kubernetes, Databricks), containerization (Docker), and model serving at low latency.
Basic qualifications:
- Advanced SQL skills and experience architecting large-scale data pipelines and feature stores.
Basic qualifications:
- Demonstrated ability to define technical roadmaps, influence direction across teams, and make architectural decisions that hold up over time.
Basic qualifications:
- Excellent communication skills with the ability to present complex technical work to executive and non-technical audiences.
Basic qualifications:
- Be cutting edge. Use the latest AI tools to develop well-designed and robust software.
Preferred qualifications:
- Experience building and scaling real-time recommendation services handling millions of requests.
Preferred qualifications:
- Expertise in A/B testing methodology, causal inference, or experimentation platforms.
Preferred qualifications:
- Familiarity with LLM-based approaches to recommendation and content understanding.
Preferred qualifications:
- Experience with MLOps practices: model monitoring, feature stores, CI/CD for ML, and automated retraining pipelines.
Preferred qualifications:
- Prior experience technically leading a team of ML practitioners and setting standards adopted by others.
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