Providing technical leadership for the Search Experience team, guiding design and implementation of shared search and retrieval systems.
Owning the technical delivery of search platform initiatives, ensuring solutions meet requirements for scalability, relevance, reliability, and maintainability.
Leading our shared search platform — expanding content search and improving relevance through vector and lexical search techniques.
Designing and developing scalable search services, data processing workflows, and microservices using technologies such as Elasticsearch, Spark, and Airflow.
Writing clean, modular, and testable code in Python and/or Java, aligned with architecture guidelines and engineering standards.
Leading design discussions, code reviews, and architecture sessions to ensure software quality and maintainability.
Mentoring and supporting engineers through pairing, code reviews, and technical coaching.
Proactively identifying technical risks, dependencies, and bottlenecks, and drive them to resolution.
Contributing to cross-team alignment, ensuring the search platform integrates cleanly with broader product and AI ecosystems.

Current expertise with Lucene, Elasticsearch, Solr, or similar search engines, with industry experience in semantic and lexical search.
Only candidates with Search Technology will be considered for this role.
Demonstrated experience acting as a technical lead on complex backend or search platform systems.
Proven track record building and scaling search systems in production environments.
Current and extensive development skills in Python and/or Java; Scala is a plus.
Solid backend engineering fundamentals: API design, data modelling, distributed systems, and performance tuning.
Proven ability to balance hands-on development with technical leadership and cross-functional coordination.
Experience with Agile or Kanban teams, collaborating across functions.
Experience building or integrating AI/LLM-powered or GenAI applications.
Familiarity with vector/embedding-based search and KNN algorithms.
Exposure to graph-based data models or knowledge graph architecture.
Experience working on internal developer platforms or shared infrastructure used by multiple teams.
Knowledge of observability best practices for distributed data systems (e.g., metrics, logs, alerts).

Software Engineering Lead

Key skills