Jobs and Careers

Lead Search Engineer

United Statesfull_timeVerifiedPosted 10 Jan 2025

About the role

Work Location Type: Hybrid

As a leading industrial distributor with operations primarily in North America, Japan and the United Kingdom, We Keep The World Working® by serving more than 4.5 million customers worldwide with products delivered through innovative technology and deep customer relationships. With 2023 sales of $16.5 billion, we’re dedicated to providing value for customers, fostering an engaging culture for team members and driving strong financial results.

Our welcoming workplace enables you to learn, grow and make a difference by keeping businesses running and their people safe. As a 2024 Glassdoor Best Place to Work and a Great Place to Work-Certified™ company, we’re looking for passionate people to join our team as we continue leading the industry over our next 100 years.

Position Details

The Grainger Search team is looking for a talented Lead Search Engineer to help build and enhance a scalable, high-performance search platform. As data volumes grow and user queries become more complex, we need someone with deep expertise in search technologies like Elasticsearch, Apache Solr, or Lucene to push our infrastructure to the next level.

In this role, you’ll focus on implementing advanced search capabilities, including vector search, natural language processing (NLP), and personalization, all aimed at improving search relevancy and user experience. You’ll collaborate closely with cross-functional teams, including data engineering and data science, to design robust data pipelines and integrate machine learning models that continuously refine search results. Strong knowledge of distributed systems, API development, and performance optimization will be key to succeeding in this role.

If you’re excited by the challenge of improving large-scale search systems and have a passion for solving complex problems, we’d love to hear from you.

You will work on

Technical Collaboration & Leadership: Providing technical leadership in search technologies, guiding cross-functional projects with data science, engineering, and infrastructure teams.
Developing Search Algorithms: Implementing advanced search algorithms that can process large datasets quickly and accurately, leveraging search engine features such as vector search, natural language processing, personalization, and other state-of-the-art technologies.
Relevancy Model Development: Collaborating with machine learning and data science teams to optimize relevancy models that improve user search experiences, incorporating feedback loops and behavioral data.
Developing APIs: Writing APIs or services to integrate relevancy feature embeddings into the search engine, and developing efficient, real-time search query logic to capitalize on these embeddings.
A/B Testing and Experimentation: Implementing frameworks for A/B testing to experiment with different search and relevancy approaches, measuring and analyzing the outcomes to drive continuous improvements.
Infrastructure Optimization: Enhancing the search infrastructure to ensure scalability and robustness as the system grows in complexity and usage.
Performance Tuning: Continuously testing and optimizing the performance of the search engine to improve query response times, accuracy, and relevancy based on defined metrics.
Integration: Integrating the search infrastructure with other services and data platforms to enable seamless data retrieval, indexing, and search performance monitoring.
Data Pipeline Management: Developing and maintaining scalable data pipelines to ensure efficient data flow, low-latency indexing, and real-time search capabilities.
Data Analysis: Analyzing search patterns, user interactions, and relevancy metrics to refine search algorithms and improve the overall user experience.
Search Result Tuning Based on Business Metrics: Collaborating with product and business teams to fine-tune search results to align with business goals like conversion, engagement, and retention.