Principal Machine Learning Engineer- Search Quality

Snowflake Snowflake · Data AI · WA-Bellevue, United States · Engineering

Snowflake is seeking a Principal Machine Learning Engineer to lead search quality initiatives. This role involves transforming search relevance measurement from heuristic to data-driven approaches, bridging traditional search with modern AI, and preparing the search technology for AI-driven agentic workflows. The engineer will focus on building and optimizing search systems, applying ML to search quality (LTR, query understanding, personalized ranking), and blending hybrid search techniques. A key aspect is developing data-driven leadership through evaluation frameworks, A/B testing, and human-in-the-loop pipelines, with a forward-looking understanding of RAG and tool-use retrieval for AI agents.

What you'd actually do

  1. Serve as the technical leader for Search Quality.
  2. Transform how we measure and improve search relevance, moving from heuristic-based approaches to a disciplined, data-driven framework.
  3. Identify key areas of investment, bridge the gap between traditional search and modern AI, and ensure that our search technology is ready for the next generation of AI-driven agentic workflows.
  4. Build and optimize search systems at Snowflake-scale or equivalent high-growth environments.
  5. Design evaluation frameworks (e.g., NDCG, MRR), A/B testing methodologies, and human-in-the-loop evaluation pipelines.

Skills

Required

  • NLP
  • LLMs
  • Information Retrieval
  • Search technologies (Lucene/Elasticsearch/OpenSearch, vector databases)
  • Machine learning for search quality (Learning to Rank, query understanding, personalized ranking)
  • Hybrid search techniques (semantic and syntactic search)
  • Evaluation frameworks (NDCG, MRR)
  • A/B testing
  • Human-in-the-loop evaluation
  • AI Agentic Frameworks
  • RAG
  • Tool-use retrieval
  • Distributed systems
  • Low-latency search results

Nice to have

  • Multi-Modal Search (text, image, code)
  • Open Source Contribution
  • User Experience Empathy

What the JD emphasized

  • 15+ years of industry experience designing, building and supporting large scale distributed services
  • Has built and optimized search systems at Snowflake-scale or equivalent high-growth environments
  • Deep, hands-on experience with search technologies (e.g., Lucene/Elasticsearch/OpenSearch, vector databases) and a proven track record of improving search relevance and ranking at scale
  • Extensive experience in machine learning specifically applied to search quality, including Learning to Rank (LTR), query understanding, and personalized ranking
  • Intimate familiarity with blending semantic (vector-based, embeddings) and syntactic search (keyword-based, BM25) to achieve state-of-the-art retrieval accuracy
  • Ability to build a disciplined approach to search quality, including the design of evaluation frameworks (e.g., NDCG, MRR), A/B testing methodologies, and human-in-the-loop evaluation pipelines
  • Demonstrated ability to translate high-level product goals into technical roadmaps and influence engineering teams to execute on a unified vision for Universal Search
  • A forward-looking understanding of how traditional search systems must evolve to support AI agents, specifically focusing on RAG (Retrieval-Augmented Generation) and tool-use retrieval
  • Strong foundation in building and scaling high-performance distributed systems that serve low-latency search results across massive, heterogeneous datasets

Other signals

  • transforming how we measure and improve search relevance
  • moving from heuristic-based approaches to a disciplined, data-driven framework
  • bridge the gap between traditional search and modern AI
  • ensure that our search technology is ready for the next generation of AI-driven agentic workflows