Research Engineer - User Identity Knowledge Graph

Netflix•Los Angeles, CA

About The Position

Netflix is one of the world's leading entertainment services, with over 300 million paid memberships in over 190 countries enjoying TV series, films and games across a wide variety of genres and languages. Members can play, pause and resume watching as much as they want, anytime, anywhere, and can change their plans at any time. We are seeking a visionary and impact-driven Research Engineer to join our team of exceptional data scientists, researchers and engineers to lead the creation of Netflix’s User Identity Knowledge Graph – a foundational, 0-1 initiative that will transform our understanding of our user base, and tackle the challenge of uncovering “Who is in front of the screen?”. This is an unique opportunity to define and shape a critical problem space from scratch, architect the applied research roadmap, and deliver insights and solutions that can power targeted commerce, personalization, and more across the Netflix ecosystem.

Requirements

Proven technical leadership in 0-1 or highly ambiguous problem spaces, with a track record of shaping research direction and delivering impactful solutions.
Deep expertise in knowledge graphs and/or graph neural nets, modern ML/DL frameworks (e.g., PyTorch, TensorFlow) and large-scale data processing (e.g., Spark, PySpark, Scala etc) as applied to end-to-end ML system development.
Exceptional communication skills, able to clearly articulate complex technical concepts and influence cross-functional partners.
Strong ownership mindset and comfort with ambiguity, thriving in environments with minimal structure and high autonomy.
PhD in Computer Science or a related field (or equivalent research experience) is strongly preferred.
Passion for impact: Demonstrated ability to connect research outcomes to business value and product strategy.

Responsibilities

Lead the 0-1 development of the User Identity Knowledge Graph, defining the vision, technical strategy, and research agenda for this greenfield initiative.
Shape the problem space: Identify and articulate the key challenges and opportunities in modeling user knowledge at scale, and translate ambiguous business needs across Netflix business domains into concrete research and engineering objectives.
Drive applied research: Design, prototype, and deploy novel machine learning and knowledge representation models that capture rich, multi-faceted user information.
Set the research roadmap: Prioritize and sequence research efforts, balancing long-term vision with near-term impact, and ensure alignment with business goals.
Collaborate cross-functionally: Partner with data scientists, engineers, product managers, and stakeholders across Netflix to ensure solutions are robust, scalable, and impactful.