Machine Learning Engineer, GAI Search Platform - Moveworks

Mountain View, CAFull-time$140k–$217kPosted Jul 16, 2026
Google Chrome Microsoft Edge Apple Safari Mozilla Firefox Machine Learning Engineer, GAI Search Platform - MoveworksFull-timeEmployee Type: RegularRegion: AMS - North America and CanadaWork Persona: Required in OfficeCompany DescriptionIt 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.Job DescriptionWhat You Will DoWe are looking for Machine learning engineers to join our GenAI Search Platform team to improve our search-based question answering systems, utilizing both traditional machine learning and cutting-edge generative AI technologies. At Moveworks, we build conversational search systems to provide instant answers to enterprise users’ questions. We deliver business intelligence support by combining modern information retrieval with the latest advancement in natural language understanding, including LLMs (large language models). By accurately retrieving, matching, ranking, and summarizing text snippets grounded in heterogeneous knowledge sources, our search products are reshaping the enterprise search experience for millions of our customers’ employees.The search platform team works closely with the ranking, product, design, infrastructure and data science teams to drive our agentic search products to the next generation of capabilities focusing on performance and enterprise readiness.We’re building a team that thrives on moving fast, solving challenging product and 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. Drive the development of our search platform with new LLM-enabled generative AI features in a fast-paced environment. This platform would enable product teams to build new applications and features quickly, ship new prototypes to customers at scale, evaluate and iterate on ranking improvements in a systematic manner, and enforce guarantees on quality, security, privacy and scalability.Develop systematic evaluation metrics and methodology for our search models, leveraging LLMs to address unique challenges in the enterprise such as security and privacy. QualificationsWhat You Bring To The Table3+ years of engineering.Passionate about helping product and ranking teams move faster and scale their solutions better in an environment where everyone cares deeply about customer impact and success.First-hand experience relevant to search platforms such as traditional information retrieval, search ranking and relevance, recommendation systems, or question answering systems.Background in text processing, information retrieval, natural language understanding, or machine learning.Nice to HaveExperience with (or desire to learn) large language models, retrieval augmented generation (RAG), and agents.Experience in Python. Contributing to mature python libraries or platform code, including open-source repositories is a nice to have.Experience in Golang (not required).For positions in this location, we offer a base pay of $139,700 - $216,500, plus equity (when applicable), variable/incentive compensation and benefits.  Sales positions generally offer a competitive On Target Earnings (OTE) incentive compensation structure.  Please note that the base pay shown is a guideline, and individual total compensation will vary based on factors such as qualifications, skill level, competencies and work location. ...

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