Director, Machine Learning Engineer, Content

Snap•Palo Alto, CA

3d•Onsite

About The Position

Snap Inc. is hiring a Director of ML to lead its Content Machine Learning Engineering team. The organization builds personalized candidate retrieval and ranking systems, along with content understanding, quality, freshness, and relevance capabilities for Stories and Spotlight. Its work includes multimodal content understanding, graph representation learning, content ranking, human review, and personalization to improve user experiences and support content creators. The Director will set multi-year machine learning strategy for recommendation and ranking, content understanding, production reliability, and experimentation. The role connects model investments to business outcomes and oversees daily operations across teams in Seattle, Los Angeles, San Francisco, Palo Alto, and New York. The Director will establish organizational goals and technical roadmaps, partner with executives and cross-functional teams, influence architecture and implementation, and build and develop high-performing engineering teams and their leaders. Snap follows a “default together” approach, expecting team members to work in an office at least four days per week. The position is eligible for equity in the form of RSUs. U.S. base salary ranges vary by pay zone: Zone A (California, Washington, and New York City), $334,000–$500,000 annually; Zone B, $317,000–$475,000 annually; and Zone C, $284,000–$425,000 annually.

Requirements

Bachelor's degree in a relevant technical field such as computer science, or equivalent practical work experience.
10+ years of post-bachelor's software engineering experience; or a master's degree in a technical field and 9+ years of post-graduate software development experience; or a PhD in a related technical field and 6+ years of post-graduate software development experience.
5+ years of experience leading a large team at scale and managing engineering with a focus on driving efficiency.
Experience working with machine learning, AI, and ranking systems.
Ability to partner with cross-functional executives and management across a geographically distributed organization.
Demonstrated experience leading and developing engineers with diverse backgrounds, skill sets, and perspectives.
Strong understanding of machine learning, deep learning approaches, and algorithms.

Nice To Haves

Track record of delivery in rapidly changing, highly collaborative, multi-site, multi-stakeholder environments.
Experience with machine learning and generative recommendations.
Experience with high-scale ranking and recommendation systems.

Responsibilities

Set multi-year ML strategy for recommendation and ranking, content understanding, production reliability, and experimentation.
Oversee day-to-day operations of Content ML teams across multiple offices.
Develop organizational-level goals and drive the engineering team's technical and organizational roadmap.
Collaborate with executives, Finance, Product, and Engineering to deliver high-quality products.
Influence architecture and implementation decisions for scalable, reliable, and cost-effective engineering solutions.
Build and scale high-performing teams of software engineers, machine learning engineers, and technical leaders.
Mentor and develop engineering managers, directors, and senior independent contributors.
Iterate quickly while maintaining quality and raising operational excellence.
Connect machine learning model investments to business outcomes.