Staff Software Engineer (search Ranking)

Databricks Databricks · Data AI · Bangalore, India · Engineering - Pipeline

Staff Software Engineer role focused on enhancing search ranking and query understanding for Databricks' AI/ML-powered products. The role involves developing and deploying ML-based search relevance models, designing NLP pipelines, and contributing to evaluation frameworks for search quality.

What you'd actually do

  1. Drive the development and deployment of ML based search and discovery relevance models and systems integrated with Databricks' products and services.
  2. Design and implement automated ML and NLP pipelines for data preprocessing, query understanding and rewrite, ranking and retrieval, and model evaluation, enabling rapid experimentation and iteration.
  3. Collaborate with product managers and cross-functional teams to drive technology-first initiatives that enable novel business strategies and product roadmaps for the search and discovery experience.
  4. Contribute to building a robust framework for evaluating search ranking improvements - both offline and online.

Skills

Required

  • 10+ years experience developing search relevance systems at scale in production or in high-impact research environments.
  • Experience applying LLM to search relevance
  • ML based search and discovery relevance models
  • ML and NLP pipelines
  • query understanding and rewrite
  • ranking and retrieval
  • model evaluation
  • search ranking improvements

Nice to have

  • M.S. or PhD preferred
  • Contributions to well-used open-source projects.

What the JD emphasized

  • enhancing search ranking
  • improving query understanding
  • building robust evals
  • 10+ years experience developing search relevance systems at scale in production or in high-impact research environments.
  • Experience applying LLM to search relevance

Other signals

  • Enhancing search ranking
  • Improving query understanding
  • Building robust evals
  • Growing the coverage of assets to enable seamless search at scale