Principal Machine Learning Engineer- Search Quality

Snowflake1 months ago

Location

US-CA-Menlo Park

Type

Full Time

Salary

USD 264,000 – 379,500

Level

Principal

Role

ML Engineer

Posted

Jan 30, 2026

Full TimePrincipal

The role

Summary

Snowflake is seeking a Principal Machine Learning Engineer to lead search quality initiatives for their internal search system powering discovery across data catalogs, marketplace, documentation, and workspaces. The role requires 15+ years of experience building large-scale distributed systems with deep expertise in search technologies, machine learning for information retrieval, and modern AI applications including RAG and vector search.

What you'll do

Technical Leadership: Serve as technical leader for Search Quality initiatives and drive strategic vision for universal search

Search Quality Framework: Transform search relevance measurement from heuristic-based to disciplined, data-driven approaches

System Architecture: Design and implement scalable search systems serving low-latency results across massive, heterogeneous datasets

ML Model Development: Build and optimize machine learning models for search ranking, query understanding, and personalized results

Hybrid Search Implementation: Develop state-of-the-art retrieval systems blending semantic and syntactic search techniques

Evaluation Framework Design: Create comprehensive evaluation methodologies including NDCG, MRR metrics and A/B testing pipelines

AI Integration: Evolve traditional search systems to support AI agents, RAG, and tool-use retrieval workflows

Cross-functional Collaboration: Partner with Product Management, Data Science, and AI teams to align technical investments with business impact

Technical Roadmap Development: Translate high-level product goals into detailed technical roadmaps and execution plans

Team Influence: Guide engineering teams toward unified vision for Universal Search across Snowflake's product ecosystem

What we look for

Technical

Distributed Systems15+ years designing, building and supporting large-scale distributed services

Search TechnologiesDeep hands-on experience with Lucene, Elasticsearch, OpenSearch, and vector databases

Machine LearningExtensive ML experience in search quality, Learning to Rank, and personalized ranking

NLP and LLMsSubject matter expertise in latest NLP developments and LLM applications to Information Retrieval

Hybrid SearchProven experience blending semantic (vector-based) and syntactic (keyword-based) search techniques

Search Quality MetricsExperience with evaluation frameworks including NDCG, MRR, and A/B testing methodologies

AI FrameworksUnderstanding of RAG (Retrieval-Augmented Generation) and agentic AI workflows

Education

Advanced DegreeMS/PhD in Computer Science, Machine Learning, Information Retrieval, or related technical field preferred

Continuous LearningCommitment to staying current with latest developments in AI, ML, and search technologies

Experience

Scale ExperienceBuilt and optimized search systems at Snowflake-scale or equivalent high-growth environments

Search RelevanceProven track record of improving search relevance and ranking at enterprise scale

Leadership ExperienceDemonstrated ability to influence engineering teams and execute unified technical vision

Cross-functional CollaborationExperience partnering with Product Management and Data Science teams

Startup MindsetActing with urgency to deliver incremental improvements while building toward long-term vision

Skills

Required skills

PythonAdvanced proficiency for ML model development and data pipeline implementation

Search EnginesDeep expertise in Lucene, Elasticsearch, or OpenSearch technologies

Machine LearningExtensive experience with Learning to Rank, neural networks, and ML evaluation

Distributed SystemsArchitecture and implementation of high-performance, scalable systems

Vector DatabasesHands-on experience with embedding storage and semantic search

NLPNatural Language Processing and Large Language Model applications

Data AnalysisStatistical analysis and A/B testing for search quality evaluation

System DesignLarge-scale architecture design for low-latency search services

Nice to have

Multi-Modal SearchExperience with text, image, and code search across different corpuses

Open Source ContributionsActive participation in search or ML open-source communities

Cloud PlatformsAWS, GCP, or Azure experience for cloud-native search deployments

KubernetesContainer orchestration for microservices architecture

Real-time SystemsStream processing and real-time indexing capabilities

User Experience DesignUnderstanding of search UX principles and user behavior analysis

Compensation & benefits

Salary

USD 264,000 – 379,500 (annual)

Stock options

Available

Benefits

Comprehensive Health Coverage

Medical, dental, and vision insurance with company contribution

Equity Compensation

Stock options and RSU grants as part of total compensation package

Flexible Time Off

Unlimited PTO policy for work-life balance

Professional Development

Learning stipend and conference attendance opportunities

Remote Work Support

Home office stipend and flexible hybrid work arrangements

Retirement Planning

401(k) with company matching contribution

Parental Leave

Generous paid parental leave for new parents

Mental Health Support

Employee assistance programs and mental wellness resources

Interview process

1
Initial Screening — Phone/video call with recruiter to discuss background, role expectations, and compensation
2
Technical Phone Screen — 45-60 minute technical discussion covering search algorithms, ML concepts, and system design
3
System Design Interview — Whiteboard/collaborative session designing large-scale search architecture and discussing trade-offs
4
ML Deep Dive — Technical interview focusing on machine learning for search, ranking algorithms, and evaluation metrics
5
Search Domain Expertise — Detailed discussion of search technologies, information retrieval, and practical implementation experience
6
Leadership & Collaboration — Behavioral interview assessing leadership style, cross-functional collaboration, and technical influence
7
Final Panel — Meet with engineering leadership and cross-functional partners to discuss vision and strategic alignment
8
Reference Checks — Verification of experience and performance with previous managers and colleagues