Overview
Remote
$100,000+
Full Time
Skills
Amazon Web Services
Apache Solr
Apache Spark
Electronic Commerce
Java
Microsoft Azure
Natural Language Processing
Semantic Search
Elasticsearch
Search Engineering
Search Technologies
Job Details
We are seeking a Principal Search Engineer with deep expertise in Java, Elasticsearch, and Solr to drive the development and optimization of our search and discovery platform. This role focuses on eCommerce search, product relevance ranking, and personalized search experiences to enhance user engagement and conversion rates. You will work with large-scale search architectures, apply machine learning techniques for relevance tuning, and collaborate with data scientists, product teams, and engineers to improve search performance and ranking models.
Architect, design, and implement scalable search solutions using Elasticsearch, Solr, and Java for eCommerce platforms.
Develop product relevance ranking algorithms, leveraging machine learning, NLP, and search tuning techniques.
Optimize query performance, indexing strategies, and ranking models to enhance search accuracy and speed.
Implement semantic search, personalized recommendations, and dynamic ranking models to improve user experience.
Work with clickstream data, behavioral analytics, and A/B testing to measure and improve search effectiveness.
Collaborate with data scientists and engineers to integrate AI-driven search enhancements into the platform.
Enhance facet filtering, auto-suggestions, synonym handling, and search result diversification for better user engagement.
Monitor search performance, troubleshoot issues, and implement improvements to support business growth.
Stay up to date with the latest advancements in search technologies, NLP, vector search, and eCommerce trends.
14+ years of experience in search engineering, with a strong focus on Elasticsearch, Solr, and Java.
Proven experience in eCommerce search, product relevance ranking, and personalized search experiences.
Expertise in query understanding, ranking signals, boosting strategies, and dynamic ranking models.
Experience with vector search, NLP, and machine learning-based search optimizations.
Deep knowledge of distributed search architectures, indexing, and scaling strategies.
Strong understanding of RESTful APIs, microservices, and cloud-based search deployments (AWS, Google Cloud Platform, or Azure).
Familiarity with big data processing frameworks (Spark, Kafka) and logging tools for search analytics.
Experience with A/B testing and search metric evaluation (CTR, MRR, NDCG, recall/precision).
Strong problem-solving skills, with the ability to diagnose and optimize complex search performance issues.
Employers have access to artificial intelligence language tools (“AI”) that help generate and enhance job descriptions and AI may have been used to create this description. The position description has been reviewed for accuracy and Dice believes it to correctly reflect the job opportunity.