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Sr. Staff Machine Learning Engineer - Foundational AI Technologies



Software Engineering, Data Science
Los Angeles, CA, USA · Sunnyvale, CA, USA
Posted on Thursday, June 13, 2024

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.

At LinkedIn, we trust each other to do our best work where it works best for us and our teams. This role offers both hybrid and remote work options. This means 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, or you can work remotely from most locations within the country listed for this role.

At LinkedIn, our Foundational AI Technologies (FAIT) organization stands as the innovation epicenter, addressing the fundamental AI challenges and the force behind LinkedIn's next-generation AI-driven member experiences. Our mission spans across the entire marketplace, leveraging our expertise in data curation, algorithm development, and robust infrastructure to spearhead AI innovations. We are dedicated to creating a transformative impact on all LinkedIn products, establishing the platform as a leader in the AI realm.

As part of the FAIT team, you will be at the heart of member understanding that redefines the way LinkedIn understands and interacts with its entities across various marketplaces. You will be building the next generation member understanding framework based on Foundation Large Language models, specifically tailored to LinkedIn's data. While we build these models, we will be creating massive scale member data sets increasing leverage and efficiency via enabling downstream models. Our models will replace several legacy Member models. We will build new serving frameworks increasing the leverage and democratization of such key services. Our goal is to have state of the art availability and freshness of our data and services.

As a Senior Staff Engineer in the Foundational AI Technologies team, you will act as the primary domain expert, and you will research, develop, build and ship cutting edge AI technologies. You are expected to provide technical leadership, and drive architectural decisions and implementation across the engineering organization.

This individual will be a core member of LinkedIn’s Foundational AI Technologies team and will partner closely with other verticals in Data and AI, and Infrastructure teams. This is a rare opportunity to lead initiatives at the cutting-edge of Data and AI, which benefits every product and team at Linkedin and over 1 Billion members of the global workforce.


  • Develop and implement the Foundation Large Language Model, customizing it to uniquely comprehend LinkedIn's diverse marketplace entities.
  • Enhance the AI system's ability to understand LinkedIn members' interests, intents, and behaviors, using advanced LLM techniques.
  • You will act as the primary domain expert to influence technology choices
  • You will research and develop cutting edge AI technologies
  • You will build and ship scalable software for AI tasks
  • You will drive architectural decisions and implementation across the engineering organization
  • You will provide technical leadership to cross-functional teams and drive alignment on technology strategy
  • You will establish a culture that values diverse viewpoints while navigating complex decisions
  • You will partner effectively with leads (ICs and managers) from other AI teams
  • You will define the bar for quality and efficiency of software systems while balancing business impact, operational impact and cost benefits of design and architectural choices
  • Lead by example to build a culture of craftsmanship and innovation
  • Be an industry thought leader. Represent LinkedIn in relevant industry forums

Basic Qualifications

  • BA/BS Degree in Computer Science or related technical discipline or equivalent practical experience
  • 5+ years of industry experience in software design, development, and algorithm related solutions
  • 5+ years experience programming languages such as Java, C/C++, Python, etc.
  • 2+ years in an architect or technical leadership position

Preferred Qualifications

  • PhD in Computer Science, Machine Learning, Statistics or related fields
  • 8+ years of experience in AI/Data Science and Large Language Models
  • Strong academic credentials with publications in top-tier journals and conferences
  • Background in one or more of the following areas: deep learning, information retrieval, knowledge graph, natural language processing, optimization
  • Experience in building large scale AI models and systems
  • Experience in large language models and deep neural network solutions
  • Demonstrated ability to work with peers in engineering across teams to set technical directions
  • Excellent communication and presentation skills

Suggested Skills:

Deep learning

Machine learning

Large language models

Data Science

Information Retrieval

LinkedIn is committed to fair and equitable compensation practices.

The pay range for this role is $180,000 to $300,000. 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

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