Engineering Lead - Extreme Retrieval

Microsoft Microsoft · Big Tech · Bengaluru, KA, IN · Software Engineering

Engineering Lead to build and own the software engineering platform for large-scale machine learning retrieval systems, including RAG for Copilots, web-scale search, and personalized recommendations. The role focuses on translating retrieval research into reusable, scalable systems, defining engineering standards, and integrating new methods. It involves cross-team technical leadership and improving developer/researcher experience.

What you'd actually do

  1. Own the architecture and long-term evolution of a shared engineering platform for retrieval systems.
  2. Own the shared repository and codebase for retrieval methods, driving its architecture, extensibility, engineering quality and long-term health.
  3. Work with researchers and partner engineering teams to integrate new retrieval methods into a reusable, scalable and well-structured codebase.
  4. Improve the developer and researcher experience through clear APIs, examples, tooling and workflows.
  5. Drive integration with downstream systems and partner teams so that retrieval methods can be adopted faster and more reliably.

Skills

Required

  • Bachelor’s or Masters in Computer Science, Engineering, or related fields.
  • Strong software engineering skills with a proven track record of designing, building, reviewing and maintaining high-quality code in large shared codebases.
  • Significant experience owning or building shared platforms, frameworks, repositories or ML systems used by multiple teams.
  • Strong communication and cross-team collaboration skills, with the ability to drive alignment .
  • High ownership, sound judgment, and the ability to independently drive ambiguous technical problems to completion.

Nice to have

  • Proven ability to work with researchers, engineers, and partner teams to turn research ideas into robust engineering systems.
  • Good understanding of machine learning systems and retrieval methods, such as dense, sparse, or hybrid retrieval, ranking/reranking, and evaluation metrics.
  • Experience building evaluation, benchmarking, or platform infrastructure for machine learning systems.
  • Familiarity with distributed systems, performance optimization, and GPU-backed ML workflows.

What the JD emphasized

  • high-impact project specializing in large-scale machine learning for consumer and enterprise retrieval
  • defining the engineering foundations that enable retrieval research to be translated into reusable, extensible and broadly adopted systems
  • setting the engineering standards, core interfaces, and evaluation framework that govern how new retrieval methods and models are built, validated and adopted
  • Proven ability to work with researchers, engineers, and partner teams to turn research ideas into robust engineering systems.
  • Experience building evaluation, benchmarking, or platform infrastructure for machine learning systems.

Other signals

  • building ML systems for retrieval
  • RAG powering Copilots
  • web-scale retrieval for search engines
  • personalized recommendations for billions of users
  • defining engineering foundations for retrieval research
  • setting engineering standards, core interfaces, and evaluation framework
  • integrating new retrieval methods into reusable, scalable codebase
  • improving developer and researcher experience
  • driving integration with downstream systems
  • cross-team technical leadership