Senior Staff Tech Lead, YouTube Shorts Discovery

info_outline X In most instances, this position requires in-person interviews as part of the hiring process.Note: By applying to this position you will have an opportunity to share your preferred working location from the following: Mountain View, CA, USA; San Bruno, CA, USA. Minimum qualifications:

• Bachelor's degree or equivalent practical experience.
• 8 years of experience in software development.
• 7 years of experience leading technical project strategy, ML design, and working with industry-scale ML infrastructure (e.g., model deployment, model evaluation, data processing, debugging, fine tuning).
• 5 years of experience with design and architecture; and testing/launching software products.
• 5 years of experience building and deploying recommendation systems models (retrieval, prediction, ranking, embedding) in production and experience building architecture in different modeling domains.

Preferred qualifications:

• Master's degree or PhD in Engineering, Computer Science, or a related technical field.
• 8 years of industry experience working on Artificial Intelligence/Machine Learning (AI/ML) recommendations.
• 8 years of industry experience in the recommendations technology domain.
• 5 years of experience in a technical leadership role leading project teams and setting technical direction.
• 5 years of experience with one or more of the following: large-scale recommendation or search systems, reinforcement learning (e.g., sequential decision making), ML infrastructure, or specialization in another ML field.

About the job In this role, you will tech-lead a team of AI/ML software engineers to grow the YouTube Shorts ecosystem by recommending Shorts that are aligned with users' dynamically changing interests. The YouTube Shorts discovery models are a suite of large-scale AI/ML systems designed to model users' interests by leveraging Google-wide data sources, understanding Shorts' content and recommending the right content to the viewers. Designed for multi-task learning across various surfaces, these models are applied to numerous downstream tasks, including retrieval, user action predictions, rich user model generation, and knowledge distillation for training more compact models. This role specializes in techniques-including LLMs, generative retrieval, and long-sequence modeling-to build foundational models for personalized retrieval and early-stage ranking. Individual pay is determined by factors including job-related skills, experience, and relevant education or training. US: $262000 - $364000 (USD) 25% bonus target equity benefits Learn more about benefits at Google . Responsibilities

• Define technical strategy for enhancing YouTube Shorts discovery models and systems to accelerate viewer and creator growth while improving user satisfaction.
• Provide technical leadership on projects; design, develop, test, and deploy large-scale recommendation models, novel model architectures, and optimize ML infrastructure to drive the growth of the Shorts ecosystem.
• Partner with engineering, product, data-science, and research teams to convert business goals into scalable technical solutions that grow the Shorts ecosystem.
• Facilitate alignment and clarity across teams on goals, prioritization, outcomes, and timelines. Mentor and influence to uplevel junior engineers on the team.