Principal Search Engineer - Lucene, Elasticsearch, Solr

Remitly

22h

About The Position

Our team is dedicated to unlocking the rich knowledge embedded within Elsevier’s content through our rich data platform; this empowers researchers, clinicians, and innovators worldwide to gain new insights, make informed decisions, and accelerate progress across research, healthcare, and life sciences. We lead the ongoing transformation of Elsevier’s vast, unstructured information into richly interconnected knowledge graphs that capture the full depth and nuance of scientific meaning. Through our dynamic knowledge discovery platform, we combine graph-powered agentic AI with advanced search technologies to deliver contextually relevant, trustworthy, and precise answers to researchers. As part of the Search team, you'll contribute to the systems and infrastructure that fuel this mission. We focus on building scalable, reliable, and high-performance retrieval systems that accelerate innovation across Elsevier’s ecosystem.

Requirements

Expertise with Lucene, Elasticsearch, Solr, or any other search engine, and have industry experience with Semantic Search.
Proven track record building search systems at scale.
Proficiency in batch processing technologies, including Spark, Spark Streaming, Airflow.
Expertise in at least one of Java, Python, Scala.
Deep understanding of distributed system design, data modeling, and performance tuning.
Strong experience with test-driven development and CI/CD practices.
Ability to independently drive technical outcomes from problem definition to deployment.
Familiarity with Agile, Kanban, or other iterative development methodologies.

Responsibilities

Leading architectural design and ensure technical consistency.
Helping lead our shared search platform – expanding content search, improving relevance via vector and lexical search techniques.
Building world-class search systems to enhance users’ search experience.
Automating processes to assist other teams.
Collaborating on new ideas to optimize systems and engineering workflows.
Building relationships with other engineering teams to identify and solve their pain points.
Working across the stack, from development to infrastructure.
Designing and developing scalable data processing workflows and microservices using Spark, Spark Streaming, and Airflow.
Writing clean, modular, and testable code in Python, Java, or Scala, aligned with coding standards and architecture guidelines.
Leading implementation of system components that span multiple services and modules.
Diagnosing and resolve complex technical issues across distributed systems and data workflows.
Leading design discussions, code reviews, and architecture sessions to ensure software quality and maintainability.
Developing and maintaining data models to support analytical and operational use cases.
Collaborating with cross-functional stakeholders to translate product requirements into reliable engineering solutions.
Contributing to interviewing, onboarding, mentoring, and technical guidance for less-senior engineers.