• Drive innovation in big data processing by contributing and adding to the team’s suite of data products and frameworks. • Partner with our world-class algorithm teams to drive continuous improvement and innovation to Netflix personalization.

• Build tools to split IDE components into client-server modules. • Provide technical solutions and architectural input for various IDE platform components, including indexing, editors, connection, SSH, Docker, the language support framework, and many others. • Lead integration projects with internal teams to implement the new version of the platform.

• Maintain and enhance cross-platform desktop GUI applications (Windows/Linux) • Develop and extend a Python-based API that integrates with the core application • Support user workflows in technically complex software environments (e.g., 3D CAD, CFD) • Ensure performance, stability, and usability of engineering-focused GUI tools • Maintain and operate the software build server and continuous integration pipelines • Manage release processes and versioned software distribution • Support and maintain license control systems and related backend services • Maintain AWS-based software distribution and business operations systems • Manage and configure AWS services, ensuring security, scalability, and reliability of cloud infrastructure

• Design, implement, and operate core backend components of the search system, spanning request-time services and background data pipelines • Contribute to document ingestion, crawling, and preprocessing workflows, adapting strategies based on source, domain, and freshness requirements • Build and evolve indexing and retrieval systems, including data formats, update strategies, and access patterns • Implement and improve search request flows, including query processing, retrieval orchestration, and response assembly under strict latency budgets • Build well-tested services and pipelines with clear responsibilities and interaction contracts, while remaining flexible as the system evolves • Define and implement observability primitives, including structured logs, metrics, traces, and quality signals for both online and offline components • Support experimentation and iteration by enabling feature flags, controlled rollouts, and online experiments • Track throughput, latency, and resource usage across the system, and improve performance or cost efficiency when business needs require it • Collaborate closely with ML engineers to integrate semantic retrieval and ranking models, while keeping ML logic decoupled from core system internals • Work with data analysts and product managers to translate product and quality goals into concrete backend behavior and measurable metrics