Staff Machine Learning Scientist – Personalization

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.
• 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.
• Design and lead the development of software used by multiple teams, ensuring long-term maintainability, scalability, and adaptability.
• 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.
• Establish and enforce experimentation best practices, including A/B testing frameworks, offline evaluation methodology, and metrics design across personalization surfaces.
• Lead team meetings, ensure the team's progress on the roadmap, and make technical decisions that unblock projects.
• 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.
• 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.
• 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.
• Expert‑level Python and deep proficiency with modern ML frameworks (PyTorch or TensorFlow) and recommendation‑specific tooling (e.g., NVTabular, Merlin, Triton).
• 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.
• Demonstrated ability to define technical roadmaps, influence direction across teams, and make architectural decisions that hold up over time.
• Excellent communication skills with the ability to present complex technical work to executive and non‑technical audiences.

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.