Sr. Staff Software Engineer - Ads Ranking AI

Company Overview: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 opportunity 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.Location:This role will be based in Sunnyvale, CA. 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 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.Team Overview:The Ads Ranking team is responsible for building and applying ML/AI solutions in the fast growing Marketing Solutions business ($5.5B+) at LinkedIn. As a Sr. Staff Software Engineer, you will be responsible for solving cutting-edge AI problems that empower large & small businesses to reach, engage, and convert professional audiences at scale. This role covers a number of challenges around: ranking personalization for multiple Ad formats, multiple placements across LinkedIn, varying objectives (Click, Video View etc), UI optimization (bandits), Visual optimization of Ads content (custom generative-AI/LLMs and content recommendations) and working in near real-time (by understanding user, campaigns and content). The team works on diverse modeling problems across deep-learning, multi-objective optimization, multi-modal content models and large parameter model training and low-latency systems to gather data + execute models in near real-time to over 1 billion LinkedIn members.Responsibilities:Work with large-scale data to build systems which will leverage and support new AI techniques for image & text generative models, multi-task learning, transfer learning, content understanding, deep learning, entity embedding and algorithms to optimize member experience and advertiser ROI in the marketplace.Provide technical leadership to junior engineers and collaborator orgs, drive and perform best engineering practices to initiate, plan, and execute large-scale, cross-functional critical projects.Hold the team to a high ML craftsmanship bar and actively reduce ML tech debt, designing systems that scale with algorithms and with time, and improve engineering productivity.Partner with data science & analytics teams, product & infrastructure teams to build new revenue and engagement driving ideas.Bring together industry research and develop opportunities to contribute back to industry research.Basic Qualifications:5+ years of industry experience in software design, development, and algorithm related solutions.2+ years in an architect or technical leadership position.BA/BS Degree in Computer Science or related technical discipline or equivalent practical experience.Experience with Machine Learning / Recommendation Systems.Preferred Qualifications:10+ years of relevant professional experience including work at the SME level (Senior Staff, Principal, Distinguished Engineer, etc.).2+ years of experience in AI/Machine Learning including work with Generative/Transformer Models, Large Language Models (LLM), and/or deep neural network models.PhD in Computer Science, Machine Learning, Statistics or related fields.Strong academic credentials with publications in top-tier journals and conferences.Background in one or more of the following areas: deep learning, knowledge graph, information retrieval, natural language processing, optimization, image/text generation.Demonstrated ability to work with peers in engineering across teams to set technical direction.Suggested Skills:Deep learning models and Transformers.Thought leadership in AI/ML.Recommendation Systems.Information Retrieval Solutions.

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