Staff AI Engineer

Staff AI Engineer This role will be based in Sunnyvale, San Francisco, or New York City. 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. AI is at the core of how LinkedIn connects more than a billion members to opportunity across jobs, sales, marketing, content, and trust platforms. As a Staff AI Software Engineer you will own end-to-end machine learning systems that run in production at LinkedIn scale. You won't just train models, you will own the recommender and classification system that power LinkedIn's core products. Your responsibilities cover the full product lifecycle from translating product requirements into system design, training the models to power the system, driving the experiments that prove efficacy, and managing the GPU fleets that run them at scale in milliseconds – you are the engine that drives value for our members. This is a lead individual-contributor role; we expect you to own all aspects of a significant workstream, align technical direction across orgs, and lead the day to day work of engineers on your own team. As owner you have the freedom to set technical directions and are held accountable for delivering measurable member and business impact with a sense of urgency. What success looks like in your first year: You lead a significant AI workstream end to end and ship at least one model-based system to a measured impact on member or business value and effectively communicate the impact to external partners. You become the go-to cross-team point of contact for your domain and onboard or mentor at least one other engineer onto the stack. You drive a meaningful efficiency or quality improvement (i.e. inference/training efficiency, engineer velocity, or tech-debt removal) involving multiple members of the team backed by data. You operate systems reliably as on-call and root-cause at least one significant production regression. Why LinkedIn: You'll work on AI systems with immediate, measurable impact on more than a billion members, alongside engineers who set the industry bar for recommendation systems at scale powered by the latest open source generative models and GPU inference. We encourage staying up to date on industry state-of-the-art and sharing your work with the community through conference, journal, and blog publications. We invest in your growth with real mentorship, we trust you with real ownership, and we measure what matters: impact, not output according to our core engineering principles. Impact: quantify the value you created for members, the business and your team Leadership: communicate and lead through influence, not authority Execution: deliver impact with a sense of urgency Craft: innovate and create leverage with high-quality solutions Qualifications Basic Qualifications Bachelor's degree in Computer Science or related technical field or equivalent practical experience 4+ years of industry experience in software design, development, and algorithm related solutions. 4+ years experience in programming languages such as Java, Python, etc. 4+ years experience with machine learning, data mining, and information retrieval or natural language processing Preferred Qualifications 6+ years of relevant AI/Machine Learning experience MS or PhD in Computer Science or related technical discipline Experience leading a significant project of 3+ AI engineers Experience with cross-functional communication to product, engineering, business or data science partners. Experience with PyTorch or similar Deep Learning frameworks Experience with Spark for data manipulation and transformation Experience with A/B testing at scale in a consumer-facing product Experience with AI code development (i.e ClaudeCode, Codex, Copilot) Experience adapting pre-trained LLMs to production systems including fine-tuning and student-teacher model paradigms Experience applying AI/ML to recommender systems at scale Published work in academic conferences or industry circles. Suggested Skills Experience leading engineers to tackle a large-scale AI problem Strong technical background & Strategic thinking Experience in Machine Learning, Big Data and Deep Learning Experience in GAI and/or LLMs Additional Information Equal Opportunity Statement We seek candidates with a wide range of perspectives and backgrounds and we are 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 committed to offering an inclusive and accessible experience for all job seekers, including individuals with disabilities. Our goal is to foster an inclusive and accessible workplace where everyone has the opportunity to be successful. If you need a Reasonable Accommodation to search for a job opening, apply for a position, or participate in the interview process, connect with us and describe the specific Accommodation requested for a disability-related limitation. Fill out an Accommodation request here: Reasonable accommodations are modifications or adjustments to the application or hiring process that would enable you to fully participate in that process. 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. San Francisco Fair Chance Ordinance Pursuant to the San Francisco Fair Chance Ordinance, LinkedIn will consider for employment qualified applicants with arrest and conviction records. 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