Tech Lead, YouTube Shorts Discovery, ML Recommendations

Google's software engineers develop the next-generation technologies that change how billions of users connect, explore, and interact with information and one another. Our products need to handle information at massive scale, and extend well beyond web search. We're looking for engineers who bring fresh ideas from all areas, including information retrieval, distributed computing, large-scale system design, networking and data storage, security, artificial intelligence, natural language processing, UI design and mobile; the list goes on and is growing every day. As a software engineer, you will work on a specific project critical to Google’s needs with opportunities to switch teams and projects as you and our fast-paced business grow and evolve. We need our engineers to be versatile, display leadership qualities and be enthusiastic to take on new problems across the full-stack as we continue to push technology forward.

In this role, you will lead the Short-form Video Exploration team and drive the success of YouTube shorts, a top strategic initiative. The Short-form Video team manages the entire ecosystem: from mobile creation tools and effects (text, stickers, filters) to a seamless, full-screen player experience. We also build the backend systems required to create, serve, and recommend these videos across Home, Search, and beyond.

Shorts Exploration is a critical component of our recommendation strategy. Our mission is to identify initial audiences for fresh content, expanding our recommendable corpus and inspiring further creation. To achieve this, we leverage advanced modeling techniques and signals to build these audiences directly within the recommendation stack.

At YouTube, we believe that everyone deserves to have a voice, and that the world is a better place when we listen, share, and build community through our stories. We work together to give everyone the power to share their story, explore what they love, and connect with one another in the process. Working at the intersection of cutting-edge technology and boundless creativity, we move at the speed of culture with a shared goal to show people the world. We explore new ideas, solve real problems, and have fun — and we do it all together.Individual pay is determined by factors including job-related skills, experience, and relevant education or training.

US: $207000 - $300000 (USD) + 20% bonus target + equity + benefits

Learn more about benefits at Google.

Responsibilities

• Design, develop, test, deploy, maintain, and enhance large scale software solutions.
  
• Provide technical leadership on high-impact projects. Manage project priorities, deadlines, and deliverables.
  
• Facilitate alignment and clarity across teams on goals, outcomes, and timelines. Influence and coach a distributed team of engineers.
  
• Lead the design and implementation of solutions in specialized ML areas, optimize ML infrastructure, and guide the development of model optimization and data processing strategies.
  

Minimum qualifications:

• Bachelor’s degree or equivalent practical experience.
  
• 8 years of experience in software development.
  
• 5 years of experience testing, and launching software products, and 3 years of experience with software design and architecture.
• 5 years of experience with one or more of the following: Speech/audio (e.g., technology duplicating and responding to the human voice), reinforcement learning (e.g., sequential decision making), ML infrastructure, or specialization in another ML field.
  
• 5 years of experience with ML design and ML infrastructure (e.g., model deployment, model evaluation, data processing, debugging, fine tuning).

Preferred qualifications:

• Master’s degree or PhD in Computer Science, Machine Learning, Artificial Intelligence, Statistics, or a related technical field.
• 8 years of experience in data structures and algorithms, with experience building distributed, large-scale machine learning systems.
• 3 years of experience in a technical leadership role, defining technical goals, setting roadmap direction, and mentoring executive engineering teams.
• Experience designing and deploying recommendation systems, personalized ranking models, or retrieval systems.
• Expertise in managing the cold-start problem, reinforcement learning, or exploration/exploitation algorithms.