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.
About the TeamThe Graph Search team is responsible for building and operating the highly scalable Search-as-a-Service platform that powers search and intelligent discovery across Netflix's internal studio and production applications. Our system streamlines search functions across diverse workflows, allowing hundreds of internal applications and thousands of studio professionals to quickly find, filter, and retrieve highly structured data.
We handle massive data volumes and complex relationships, moving data from transactional source systems into eventually consistent search indices in near-real-time. Most recently, our team has expanded into the intersection of search and AI, taking ownership of intelligent chat-based interfaces that allow users to discover and explore content data using natural language.
The Scope of WorkAs an engineer on our team, your tasks will span standard backend engineering as well as deep distributed systems challenges. You will work on optimizing high-throughput data pipelines, managing a massive index topology across large search clusters, and introducing intelligent query-orchestration mechanisms.
Check out the following tech blogs to learn more about the Graph Search platform:
How Netflix Content Engineering makes a federated graph searchable
How Netflix Content Engineering makes a federated graph searchable (Part 2)
Reverse Searching Netflix’s Federated Graph
The AI Evolution of Graph Search at Netflix: From Structured Queries to Natural Language
See the video to learn more about the entire Infrastructure & Solution team: Content Infrastructure & Solutions Team
Key ResponsibilitiesFeature Development and Ownership: Implement and own the lifecycle of backend features, search orchestration layers, and data-indexing pipelines end-to-end.
Distributed Systems at Scale: Participate in managing, scaling, and optimizing search cluster architecture.
Performance Engineering: Collaborate on throughput optimizations, sharding strategies, data consistency, and low-latency metrics for real-time data movement.
Technical Collaboration: Participate in code reviews, technical design discussions, and operational improvements to ensure high developer effectiveness.
Testing and Automation: Maintain robust testing practices (unit, integration, and end-to-end) to support high confidence in a continuous deployment culture.
Core Software Engineering: Solid foundational knowledge of backend software engineering, typically using Java or similar object-oriented languages.
Distributed Infrastructure Aptitude: A strong interest or foundational experience in large-scale data processing, data partitioning, sharding, and real-time messaging workflows.
Search and Indexing Exposure: Foundational knowledge or direct experience working with search technologies such as OpenSearch or Elasticsearch, as well as graph databases such as AWS Neptune.
Operational Mindset: An understanding of production environments, with a dedication to writing clean, maintainable, and performant code supported by robust testing and monitoring.
Effective Communication and Growth: A motivated problem-solver who communicates technical updates clearly, actively seeks feedback, and is comfortable navigating technical ambiguity.
Prior experience building centralized platforms, frameworks, or shared tooling intended for other software engineers to consume.
Foundational full-stack or UI experience (TypeScript, React, GraphQL) to contribute to self-service portals and search interfaces.
Interest or experience in graph-based data stores (such as AWS Neptune) or hierarchical data models.
An interest in learning or applying AI patterns (such as Retrieval-Augmented Generation/RAG, model observability, or agentic workflows) to expand natural language search functionality.
Languages: Java, Python
Frameworks: SpringBoot, ReactJS
APIs: GraphQL, gRPC, REST
Messaging: Apache Kafka
Workflow and Data Processing: Netflix Conductor, Apache Flink
Databases: OpenSearch, Elasticsearch, AWS Neptune, Cassandra, PostgreSQL
Cloud Platform: AWS
Inclusion is a Netflix value and we strive to host a meaningful interview experience for all candidates. If you want an accommodation/adjustment for a disability or any other reason during the hiring process, please send a request to your recruiting partner.
We are an equal-opportunity employer and celebrate diversity, recognizing that diversity builds stronger teams. We approach diversity and inclusion seriously and thoughtfully. We do not discriminate on the basis of race, religion, color, ancestry, national origin, caste, sex, sexual orientation, gender, gender identity or expression, age, disability, medical condition, pregnancy, genetic makeup, marital status, or military service.
Skills Required
- Solid foundational knowledge of backend software engineering, typically using Java or similar object-oriented languages
- Experience with Java
- Experience with Python
- Foundational experience or strong interest in large-scale data processing, partitioning, sharding, and real-time messaging workflows
- Exposure to search technologies such as OpenSearch or Elasticsearch and graph databases such as AWS Neptune
- Operational mindset: production experience, monitoring, maintainable performant code, and robust testing practices (unit, integration, end-to-end)
- Strong collaboration and communication skills; comfortable with technical ambiguity and feedback-driven growth
- Experience with Apache Kafka
- Familiarity with workflow and stream processing tools such as Netflix Conductor or Apache Flink
- Experience with databases such as Cassandra or PostgreSQL
- Prior experience building centralized platforms, frameworks, or shared tooling for other engineers
- Foundational full-stack or UI experience (TypeScript, React, GraphQL)
- Interest or experience in graph-based data stores or hierarchical data models
- Interest or experience applying AI patterns (RAG, model observability, agentic workflows) to natural language search
Netflix Compensation & Benefits Highlights
The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about Netflix and has not been reviewed or approved by Netflix.
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Fair & Transparent Compensation — Compensation is positioned as “personal top of market” with regular recalibration and broad posted ranges for senior roles that signal the philosophy. The cash‑forward structure and clearly described pay‑mix choices help set expectations on how pay is determined.
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Equity Value & Accessibility — Employees can choose the mix of cash versus fully vested 10‑year stock options, with grants structured to be retained even after departure. This employee‑directed design increases accessibility and control over equity participation.
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Healthcare Strength — Health coverage is described as comprehensive across medical, dental, vision, and mental health, with employer funding designed to offset premiums. Additional resources like counseling/coaching and wellness support reinforce breadth in care access.
Netflix Insights
What We Do
Netflix is the world's leading streaming entertainment service with 209 million paid memberships in over 190 countries enjoying TV series, documentaries and feature films across a wide variety of genres and languages. Members can watch as much as they want, anytime, anywhere, on any internet-connected screen. Members can play, pause and resume watching, all without commercials or commitments.







