Distinguished Software Engineer, Machine Learning (Growth)

LinkedIn•Mountain View, CA

4d•Hybrid

About The Position

As a Distinguished Engineer at LinkedIn, you will architect and lead the next generation of AI solutions that power LinkedIn’s growth ecosystem – from PYMK to Notifications to Agentic Onboarding – by employing the developing state of the art technologies – from reinforcement learning to generative AI and large models. You will develop strategies to drive user retention throughout the user lifecycle across LinkedIn’s consumer ecosystem, helping accelerate LinkedIn’s vision as a leader within the Growth team.

Requirements

Bachelor's degree in Computer Science or a related field, or equivalent experience
10+ years of relevant work experience
Experience leading technical projects with multiple stakeholders

Nice To Haves

PhD in a relevant field or related discipline (machine learning, statistics, computer science etc.), or equivalent research experience
10+ years experience in building engaging AI-powered consumer products at scale, with at least 5 of those years in a technical leadership position
Experience in designing and optimizing recommender systems that create highly personalized experiences for LinkedIn’s members and scale to solve complex business problems
Experience in leading large scale development projects from concept to multiple releases in production
Experience of technical leadership guiding and mentoring engineering and operations talent
Experience leading high-impact, cross-company initiatives
Dream big. Make big, bold moves to define and build the next generation of products

Responsibilities

Help define business strategy and technical strategy and drive alignment cross functionally.
Head cross team/cross functional discussions and drive alignment on product/technology strategy.
Help build highly scalable systems and applications to enable the organization to achieve its goals.
Actively improve the level of craftsmanship at LinkedIn by developing best practices and defining best strategies.
Define the bar for quality and efficiency of software systems while balancing business impact, operational impact and cost benefits of design and architectural choices.
Establish a culture that values diverse viewpoints while navigating complex decisions.