Staff AI Engineer

LinkedIn•Mountain View, CA

11h•$175,000 - $287,000•Hybrid

About The Position

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

Requirements

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

Nice To Haves

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.
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

Responsibilities

Own end-to-end machine learning systems that run in production at LinkedIn scale.
Own the recommender and classification system that power LinkedIn’s core products.
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.
Own all aspects of a significant workstream.
Align technical direction across orgs.
Lead the day to day work of engineers on your own team.
Set technical directions and are held accountable for delivering measurable member and business impact with a sense of urgency.
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.
Become the go-to cross-team point of contact for your domain and onboard or mentor at least one other engineer onto the stack.
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.
Operate systems reliably as on-call and root-cause at least one significant production regression.