Staff Machine Learning Scientist – Personalization

Das ist der Job

Propose and deliver R&D that directly shapes roadmaps, multiple projects, and long-term deliverables.

Darum lohnt es sich

Responsibilities 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. Models are used over the long term by multiple products and teams.

Design and lead the development of software used by multiple teams, ensuring long-term maintainability, scalability, and adaptability. Adapt systems to changing business needs and resolve multi-product, multi-team service incidents. Lead team meetings, ensure the team's progress on the roadmap, and make technical decisions that unblock projects.

Foster a collaborative and high-performance team culture. Mentor senior and mid-level scientists, setting high code quality standards and best practices for the team. Expert‑level Python and deep proficiency with modern ML frameworks (PyTorch or TensorFlow) and recommendation‑specific tooling (e.g., NVTabular, Merlin, Triton).

Demonstrated ability to define technical roadmaps, influence direction across teams, and make architectural decisions that hold up over time.

Core Competencies Demonstrates expertise in defining technical roadmaps for personalization and recommender systems, with a strong focus on delivering scalable solutions and managing cross‑team collaborations.

Proficient in mentoring and fostering a high‑performance team culture while ensuring adherence to best practices in experimentation and model deployment. #J-18808-Ljbffr Ensure complex, multi-service personalization products meet SLAs and provide correct results over time.

Establish and enforce experimentation best practices, including A/B testing frameworks, offline evaluation methodology, and metrics design across personalization surfaces. Manage stakeholders' expectations with data-driven narratives and communicate effectively with senior leadership to align on strategy and track progress.

Drive organizational efficiency and business impact by implementing new technologies and processes. Stay current with advances in recommender systems, LLMs for personalization, and representation learning, bringing relevant advances into production when they deliver measurable improvement.

Requirements 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.

Deep expertise in recommender systems, personalization, ranking/retrieval, or computational advertising, with a track record of shipping systems that operate at scale. Strong experience with cloud‑based ML infrastructure (AWS, Kubernetes, Databricks), containerization (Docker), and model serving at low latency.

Advanced SQL skills and experience architecting large‑scale data pipelines and feature stores. Excellent communication skills with the ability to present complex technical work to executive and non‑technical audiences.