Machine Learning Engineer, GAI Search Relevance - Moveworks

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          "text": "<p>Company Description</p><p>It all started in sunny San Diego, California in 2004 when a visionary engineer, Fred Luddy, saw the potential to transform how we work. Fast forward to today — ServiceNow stands as a global market leader, bringing innovative AI-enhanced technology to over 8,100 customers, including 85% of the Fortune 500®. Our intelligent cloud-based platform seamlessly connects people, systems, and processes to empower organizations to find smarter, faster, and better ways to work. But this is just the beginning of our journey. Join us as we pursue our purpose to make the world work better for everyone.</p><p>Job Description</p><p><strong>What You Will Do</strong></p><p>We are looking for a search relevance engineer to work with a team to improve a search based question answering platform. At Moveworks, we build search technology to answer enterprise users’ tech questions instantly, by combining modern information retrieval methodology with the latest advancement in natural language understanding. Our core Search product value is achieved through the capability of accurately retrieving, matching and ranking text snippets extracted from heterogeneous knowledge sources, as direct answers to users’ requests. You will play a critical role in boosting answer quality by meeting the challenges such as lexical gap, semantic text understanding, and data sparsity.</p><p>We’re building a team that indexes on moving fast, solving challenging product/engineering problems and providing value to our customers. To be successful, you'll be partnering with product, user experience, customer success and other engineering teams to identify, define and build elegant solutions.</p><ul><li>Craft new ranking features and optimizing existing ones to enhance relevance metrics</li><li>Develop algorithm framework to support diversified ranking features</li><li>Design platform and tooling to enable parallel ranking experiments</li><li>Develop metrics to evaluate ranking performance</li></ul><p>&#xa0;</p>",
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