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职位Snowflake

Principal Machine Learning Engineer- Search Quality

Snowflake

Principal Machine Learning Engineer- Search Quality

Snowflake

US-CA-Menlo Park

·

On-site

·

Full-time

·

2mo ago

必备技能

Machine Learning

NLP

Information Retrieval

Distributed Systems

Search Systems

Learning to Rank

Snowflake is about empowering enterprises to achieve their full potential — and people too. With a culture that’s all in on impact, innovation, and collaboration, Snowflake is the sweet spot for building big, moving fast, and taking technology — and careers — to the next level.

Snowflake is about empowering enterprises to achieve their full potential — and people too. With a culture that’s all in on impact, innovation, and collaboration, Snowflake is the sweet spot for building big, moving fast, and taking technology — and careers — to the next level.

As Snowflake expands its product ecosystem, the ability for users to find relevant information across a fragmented landscape of data and metadata is paramount. The Snowscope team is at the heart of this mission, building and maintaining the internal search system that powers discovery across diverse corpuses, including the Catalog, Marketplace, Documentation, Workspaces, Notebooks, and more. We also maintain Universal Search, providing a seamless, single-entry search experience across all categories

We are looking for a Principal Software Engineer to serve as the technical leader for Search Quality. This individual will be responsible for transforming how we measure and improve search relevance, moving from heuristic-based approaches to a disciplined, data-driven framework. You will 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.

OUR IDEAL PRINCIPAL SOFTWARE ENGINEER WILL HAVE:

  • 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/Open Search, 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.

NICE TO HAVE:

  • 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.

WHY JOIN THE ENGINEERING TEAM AT SNOWFLAKE?

  • Build an industry-leading Cloud Data and AI Platform.

  • Solve challenging technical problems related to security, parallel and distributed systems, programming, resource management, large-scale system maintenance, and more!

  • Work closely with our customers & partners, understand their use cases & needs, think strategically to seek the right problem to solve at the right time, and innovate with rigor.

  • Join a world-class team of both industry veterans and rising stars.

Snowflake is growing fast, and we’re scaling our team to help enable and accelerate our growth. We are looking for people who share our values, challenge ordinary thinking, and push the pace of innovation while building a future for themselves and Snowflake.

How do you want to make your impact?

For jobs located in the United States, please visit the job posting on the Snowflake Careers Site for salary and benefits information: careers.snowflake.com http://careers.snowflake.com

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关于Snowflake

Snowflake

Snowflake

Public

snowflake provides web applications and web hosting services.

1-50

员工数

Zürich

总部位置

$70B

企业估值

评价

3.9

10条评价

工作生活平衡

3.2

薪酬

4.1

企业文化

4.0

职业发展

3.4

管理层

3.1

72%

推荐给朋友

优点

Innovative and cutting-edge technology

Supportive team and colleagues

Good benefits and compensation

缺点

Fast-paced and high-pressure environment

Heavy workload and long hours

Management and communication issues

薪资范围

2,063个数据点

Junior/L3

L3

L4

L5

L6

Mid/L4

Senior/L5

Staff/L6

Junior/L3 · Data Scientist

277份报告

$252,858

年薪总额

基本工资

$171,306

股票

$57,741

奖金

$23,811

$190,229

$354,557

面试经验

6次面试

难度

3.0

/ 5

时长

14-28周

面试流程

1

Application Review

2

Online Assessment

3

Technical Phone Screen

4

Technical Interview

5

Final Interview Round

常见问题

Coding/Algorithm

Technical Knowledge

System Design

Behavioral/STAR