Snowflake · 1 week ago
Principal Machine Learning Engineer- Search Quality
US-CA-Menlo Park
Full-time
Onsite
Lead/Staff
$264K/yr - $380K/yr
15+ years expSnowflake is about empowering enterprises to achieve their full potential — and people too. The Principal Machine Learning Engineer will serve as the technical leader for Search Quality, responsible for transforming how search relevance is measured and improved, utilizing a disciplined, data-driven framework while bridging traditional search with modern AI.
AnalyticsArtificial Intelligence (AI)Cloud Data ServicesData ManagementEnterprise SoftwareSoftware
Responsibilities
Transform how we measure and improve search relevance, moving from heuristic-based approaches to a disciplined, data-driven framework
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
Build and optimize search systems at Snowflake-scale or equivalent high-growth environments
Act with urgency to deliver incremental improvements while building toward a long-term vision
Serve as a subject matter expert in the latest developments in NLP, LLMs, and their application to Information Retrieval
Have 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
Possess extensive experience in machine learning specifically applied to search quality, including Learning to Rank (LTR), query understanding, and personalized ranking
Have intimate familiarity with blending semantic (vector-based, embeddings) and syntactic search (keyword-based, BM25) to achieve state-of-the-art retrieval accuracy
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
Translate high-level product goals into technical roadmaps and influence engineering teams to execute on a unified vision for Universal Search
Understand how traditional search systems must evolve to support AI agents, specifically focusing on RAG (Retrieval-Augmented Generation) and tool-use retrieval
Build and scale high-performance distributed systems that serve low-latency search results across massive, heterogeneous datasets
Partner with and influence Product Management and Data Science and AI teams to define quality metrics and align technical investments with business impact
Qualification
Required
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
Possesses a startup mindset, acting with urgency to deliver incremental improvements while building toward a long-term vision
Is a subject matter expert in the latest developments in NLP, LLMs, and their application to Information Retrieval
Search Domain Expertise: 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
Deep ML Expertise: Extensive experience in machine learning specifically applied to search quality, including Learning to Rank (LTR), query understanding, and personalized ranking
Hybrid Search Techniques: Intimate familiarity with blending semantic (vector-based, embeddings) and syntactic search (keyword-based, BM25) to achieve state-of-the-art retrieval accuracy
Data-Driven Leadership: 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
Technical Visionary: Demonstrated ability to translate high-level product goals into technical roadmaps and influence engineering teams to execute on a unified vision for Universal Search
AI Agentic Frameworks: 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
Distributed Systems: Strong foundation in building and scaling high-performance distributed systems that serve low-latency search results across massive, heterogeneous datasets
Cross-Functional Collaboration: Proven ability to partner with and influence, Product Management and Data Science and AI team to define quality metrics and align technical investments with business impact
Preferred
Multi-Modal Search: Experience with multi-modal search (text, image, code) and understanding of how different corpuses (like Notebooks vs. Documentation) require specialized retrieval strategies
Open Source Contribution: Active contributions to the search or ML open-source community
User Experience Empathy: A strong sense of how search quality directly impacts the end-user experience and the ability to advocate for the user in architectural decisions
Company
Snowflake
Snowflake is a cloud data platform that provides a data warehouse as a service designed for the cloud.
5001-10000 employees
H1B Sponsorship
Snowflake has a track record of offering H1B sponsorships. Please note that this does not
guarantee sponsorship for this specific role. Below presents additional info for your
reference. (Data Powered by US Department of Labor)
Distribution of Different Job Fields Receiving Sponsorship
Represents job field similar to this job
Trends of Total Sponsorships
2025 (428)
2024 (281)
2023 (154)
2022 (182)
2021 (113)
2020 (98)
Funding
Current Stage
Public CompanyTotal Funding
$2.03BKey Investors
Dragoneer Investment Group,Global Secure Invest,Salesforce VenturesSequoia CapitalAltimeter Capital,ICONIQ Growth,Sequoia Capital
2022-04-19Post Ipo Equity· $621.46M
2020-09-16IPO
2020-02-07Series G· $479M
Leadership Team
Sridhar Ramaswamy
Chief Executive Officer
Benoit Dageville
Snowflake Co-founder, President of Product
Recent News
2026-02-07
2026-02-07
Company data provided by crunchbase