Senior Software Engineer - Search Relevance in Es|ql - Elasticsearch

Elastic Elastic · Enterprise · United States · Platform - Elasticsearch

Senior Software Engineer for Elastic's Search Relevance team, focusing on improving search engine speed and relevance, including vector search capabilities. The role involves leading initiatives, contributing to Elasticsearch core, and working with community members. Requires strong Java skills and experience with search or vector databases.

What you'd actually do

  1. Lead initiatives within Elasticsearch to produce an industry-leading search engine offering, supplying unparalleled speed and relevance in search.
  2. Contribute to Elasticsearch full time, building new search features and fixing intriguing bugs, all while making the code easier to understand. Sometimes you'll need to invent a new algorithm or data structure. Or find one and implement it. Sometimes you'll need to get close to the operating system and hardware.
  3. Work with a globally distributed team of experienced engineers focused on the vector search capabilities of Elasticsearch.
  4. Be an expert on Elasticsearch search relevance. You'll identify and drive improvements this area based on your questions and your instincts.
  5. Work with community members from all over the world on issues and pull requests, sometimes triaging them and handing them off to other experts and sometimes handling them yourself.

Skills

Required

  • Professional experience with search or vector databases
  • HNSW, IVF, or other relevant algorithms and libraries on search platforms at scale
  • Solid skills in core Java
  • Standard library of data structures and concurrency constructs
  • Lambdas
  • Autonomy and project ownership
  • Collaborative development
  • Experience with data storage technologies like Elasticsearch, Solr, PostgreSQL, MongoDB, or Cassandra
  • Proven track record of using AI to accelerate development, debug complex systems, and optimize code
  • Ability to collaborate across functions and teams
  • Seamlessly transition between different projects, codebases, or teams
  • Work autonomously and can drive decisions and results in a distributed team
  • Leveraging asynchronous, direct, and transparent communication

Nice to have

  • Built things with Elasticsearch before
  • Worked with open source projects
  • Familiarity with different styles of source control workflow and continuous integration
  • Experience with data storage technology
  • Experience designing, leading, and owning cross-functional initiatives

What the JD emphasized

  • vector search capabilities
  • search relevance
  • vector databases
  • search platforms at scale
  • core Java
  • standard library of data structures and concurrency constructs
  • using AI to accelerate development
  • debug complex systems
  • optimize code
  • open source projects
  • different styles of source control workflow and continuous integration

Other signals

  • Search Relevance
  • Vector Search
  • AI to accelerate development