Senior Staff AI Engineer, Network Growth AI

Location:

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.

This role will be based in Sunnyvale CA.

Team Overview: 

The Network Growth and Relationship AI team is at the forefront of creating cutting-edge AI-powered solutions that drive meaningful connections and foster professional growth. Our team builds scalable machine learning models and advanced AI systems that help millions of LinkedIn members expand their networks discover new opportunities and deepen professional relationships. By leveraging vast data and deploying sophisticated algorithms we enhance member experience with personalized recommendations insights and connections that transform their career paths. Our recommender systems have adopted the latest modeling techniques including Sequence Modeling LLM EBR GNN etc and were continuing our journey as the modeling innovation pioneer at LinkedIn to build both ranking and retrieval models that impact the entire LinkedIn ecosystem.

The Network Growth AI team is highly impactful and is in charge of optimizing member value helping them to build relationships on LinkedIn and advance their professional network. 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 Network Growth AI is the place you should look. We also publish in top machine learning conferences.  

Responsibilities:

As a senior technical leader in the Network Growth 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 Generative Recommendation Large Language Models Graph Neural Networks and Sequential 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.
 

• You will be responsible for teams core modeling effort and our mid/long term direction

• As a hands-on tech lead you are expected to actively participate in key technical and design discussions with technical leads in the team.

• Collaborate with platform engineering product data science and partner teams to design machine learning solutions to power Network Growth ecosystem and optimize member experience.

• Operate best engineering and scientific practices & processes to ensure productivity of the team and drive faster iterations via A/B experiments.

• You will be expected to be a role model and professional coach for engineers with a strong bias for action and focus on craftsmanship.

• You will work with peers across teams to support and leverage a shared technical stack.

• You will coach the team to produce high-quality software that is unit tested code reviewed and checked in regularly for continuous integration.

Qualifications :

Basic Qualifications:

• 2 years of experience as a Technical Lead

• 5 years of overall industry experience in AI / Machine Learning 

• Bachelors Masters or PhD Degree in Computer Science Machine Learning or related technical discipline or equivalent practical experience
   

Preferred Qualifications: 

• 10 years of industry experience.

• 4 years of technical leadership (Staff) experience including recent experience at the Senior Staff / L7 / Principal Engineer level. 

• Ph.D. in Computer Science Machine Learning Natural Language Processing or a related discipline.

• Prior experience with large scale ML data infrastructure

• Experience with developing and designing production scale recommender system products.

• Published work in academic conferences or industry circles. 

Suggested Skills:

• AI Recommendation Systems 

• Transformer Models 

• Technical Leadership

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.
 

Compensation:

LinkedIn is committed to fair and equitable compensation practices. The pay range for this role is $191000 - $315000. Actual compensation packages are based on a wide array of factors unique to each candidate including but not limited to skill set years & depth of experience certifications and specific office location. This may differ in other locations due to cost of labor considerations.

The total compensation package for this position may also include annual performance bonus stock benefits and/or other applicable incentive compensation plans. For additional information visit: 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.

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• 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

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Pursuant to the San Francisco Fair Chance Ordinance LinkedIn will consider for employment qualified applicants with arrest and conviction records.

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No

Employment Type :

Full-time