LinkedIn is the world’s largest professional network, built to help members of all backgrounds and experiences achieve more in their careers. Our vision is to create economic opportunities for every member of the global workforce. Every day our members use our products to make connections, discover opportunities, build skills and gain insights. We believe amazing things happen when we work together in an environment where everyone feels a true sense of belonging, and that what matters most in a candidate is having the skills needed to succeed. It inspires us to invest in our talent and support career growth. Join us to challenge yourself with work that matters. At LinkedIn, we trust each other to do our best work where it works best for us and our teams. This role offers a hybrid work option, meaning you can both work from home and commute to a LinkedIn office, depending on what’s best for you and when it is important for your team to be together. LinkedIn is looking for a Distinguished Engineer to join the LLM Serving and Semantic Search Infrastructure. In this role, you will lead the development of LinkedIn’s large language model (LLM) serving and semantic search infrastructure, driving innovation and efficiency in these critical areas to support LinkedIn's future growth. This role will focus on building and optimizing the infrastructure necessary to power LLM inference engines and semantic search at scale, ensuring high performance across LinkedIn’s global platform. As the DE you will play a pivotal role in shaping the technical direction for LLM serving and semantic search, making rapid progress in areas that are essential for the success of LinkedIn’s search capabilities and overall platform performance. You will collaborate with cross-functional teams, including engineering and product teams, to deliver best-in-class search and AI-powered solutions. Key Responsibilities: Lead the design, development, and deployment of LinkedIn’s LLM serving infrastructure and semantic search systems. Drive architectural decisions to optimize the performance of LLM inference engines across both CPU and GPU environments, ensuring scalability and efficiency. Develop and implement strategies to power LLM-based search and semantic search solutions, enabling LinkedIn’s business growth and improving member experiences. Collaborate closely with engineering and product teams to align LLM serving and search infrastructure with LinkedIn’s product roadmap and overall business objectives. Build and scale the infrastructure necessary for high-performance LLM inference and search serving, ensuring low latency and high availability across LinkedIn’s global network. Lead a team of engineers, providing mentorship and technical direction to build robust and scalable solutions that support LinkedIn’s future search capabilities. Stay ahead of industry trends in LLM infrastructure, search technologies, and AI, ensuring LinkedIn adopts cutting-edge approaches to maintain leadership in the field. Basic Qualifications: -Proven experience in building large-scale search infrastructures and LLM inference engines, with a demonstrated ability to deliver high-performance, scalable systems. -Deep expertise in developing infrastructure to support both CPU and GPU environments for LLM inference. -Experience leading the development of search systems, semantic search solutions, or related AI-powered infrastructures at a global scale. -Strong technical leadership skills, with experience mentoring teams and driving large-scale projects to successful completion. -Bachelor’s degree in Computer Science, Engineering, or a related technical field. Preferred Qualifications: -Master’s or PhD in Computer Science or related field. -Experience building search infrastructure at leading tech companies, with a history of developing cutting-edge AI and search technologies. -Expertise in optimizing large language model serving and search systems for both enterprise and consumer-facing platforms. -Strong understanding of modern AI, machine learning, and natural language processing (NLP) techniques, particularly related to semantic search. "Suggested Skills" - Search Infra. - Developing technology strategies at a company-wide scale. - Technical leadership LinkedIn is committed to fair and equitable compensation practices. The pay range for this role is $238,000 to $390,000. Actual compensation packages are based on several factors that are unique to each candidate, including but not limited to skill set, depth of experience, certifications, and specific work location. This may be different in other locations due to differences in the cost of labor. The total compensation package for this position may also include an annual performance bonus, stock, benefits and/or other applicable incentive compensation plans. For more information, visit https://careers.linkedin.com/benefits. Equal Opportunity Statement LinkedIn is committed to diversity in its workforce and is proud to be an equal opportunity employer. LinkedIn considers qualified applicants without regard to race, color, religion, creed, gender, national origin, age, disability, veteran status, marital status, pregnancy, sex, gender expression or identity, sexual orientation, citizenship, or any other legally protected class. LinkedIn is an Affirmative Action and Equal Opportunity Employer as described in our equal opportunity statement here: https://microsoft.sharepoint.com/:b:/t/LinkedInGCI/EeE8sk7CTIdFmEp9ONzFOTEBM62TPrWLMHs4J1C\_QxVTbg?e=5hfhpE. 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Examples of reasonable accommodations include but are not limited to: -Documents in alternate formats or read aloud to you -Having interviews in an accessible location -Being accompanied by a service dog -Having a sign language interpreter present for the interview A request for an accommodation will be responded to within three business days. However, non-disability related requests, such as following up on an application, will not receive a response. LinkedIn will not discharge or in any other manner discriminate against employees or applicants because they have inquired about, discussed, or disclosed their own pay or the pay of another employee or applicant. 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