Job Description: Company Overview: LinkedIn is the world’s largest professional network, built to create economic opportunity for every member of the global workforce. Our products help people make powerful connections, discover exciting opportunities, build necessary skills, and gain valuable insights every day. We’re also committed to providing transformational opportunities for our own employees by investing in their growth. We aspire to create a culture that’s built on trust, care, inclusion, and fun – where everyone can succeed. Join us to transform the way the world works. This role will be based in Sunnyvale. At LinkedIn, our approach to flexible work is centered on trust and optimized for culture, connection, clarity, and the evolving needs of our business. The work location of this role is hybrid, meaning it will be performed both from home and from a LinkedIn office on select days, as determined by the business needs of the team. Team Overview: The Notification AI team builds high-quality large scale machine learnt models to deliver the right content to the right member through the right channel at the right time and the right frequency. This enables LinkedIn to have a healthy notification ecosystem and allows members to accomplish their professional goals on time through a pleasant experience. And for product teams, we provide a targeting platform that delivers product’s value to members. As a tech lead in the Notifications AI team, you will directly impact member experience through optimizing the above dimensions. You will be responsible for leading a team of scientists and machine learning engineers that build and own personalization algorithms, models, and systems. You will work with some of the best engineers and scientists on state-of-the-art technology that leverages truly big data. You will be leading the core modeling initiatives in the team, including our efforts in Multi Objective Reinforcement Learning, Decision Transformers, Meta Learning, and Large Language models. You are expected to challenge the status quo on AI, Engineering, and Product fronts, propose innovative new ideas, and lead these new initiatives to production to further improve our member experience and drive value. The Notifications AI team is highly impactful and is in charge of optimizing member value, bringing members back to LinkedIn and keeping them engaged with the ecosystem. The team works in close collaboration with the product, engineering and data science team and has a very exciting roadmap ahead. If you are looking to lead a highly visible team that operates at a fast pace, works on exciting research problems and delivers great results every quarter, Notifications AI is the place you should look. We also regularly publish in top machine learning conferences. Responsibilities: Work with large-scale data to build systems which will leverage and support new AI techniques for multi-task learning, transfer learning, content understanding, deep learning, entity embedding and algorithms to optimize member experience. 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 innovative ideas to improve member experience. Bring together industry research and develop opportunities to contribute back to industry research Basic Qualifications: 2+ years in a Lead Engineer, Architect, or Technical Leadership position. 5+ years of industry experience in software design, development, and algorithm related solutions 5+ years experience programming languages such as Java and/or Python BA/BS Degree in Computer Science or related technical discipline or equivalent practical experience Experience with Machine Learning or Recommendation Systems Preferred Qualifications Ph.D. in Computer Science, Information Retrieval, Machine Learning, Natural Language Processing, or a related discipline. Worked with web-scale traffic and data. Experience with developing and designing production scale recommender system products. Published work in academic conferences or industry circles. 10+ years of industry experience. Suggested Skills: Deep learning models and transformers Machine Learning Recommendation systems You will Benefit from our Culture: We strongly believe in the well-being of our employees and their families. That is why we offer generous health and wellness programs and time away for employees of all levels. LinkedIn is committed to fair and equitable compensation practices. The pay range for this role is $191,000 - $315,000. 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 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. Please reference https://www.eeoc.gov/sites/default/files/2023-06/22-088\_EEOC\_KnowYourRights6.12ScreenRdr.pdf and https://www.dol.gov/ofccp/regs/compliance/posters/pdf/OFCCP\_EEO\_Supplement\_Final\_JRF\_QA\_508c.pdf for more information. 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