Senior Engineering Manager, YouTube, Recommendations ML

Like Google's own ambitions, the work of a Software Engineer goes beyond just Search. Software Engineering Managers have not only the technical expertise to take on and provide technical leadership to major projects, but also manage a team of Engineers. You not only optimize your own code but make sure Engineers are able to optimize theirs. As a Software Engineering Manager you manage your project goals, contribute to product strategy and help develop your team. Teams work all across the company, in areas such as information retrieval, artificial intelligence, natural language processing, distributed computing, large-scale system design, networking, security, data compression, user interface design; the list goes on and is growing every day. Operating with scale and speed, our exceptional software engineers are just getting started -- and as a manager, you guide the way. With technical and leadership expertise, you manage engineers across multiple teams and locations, a large product budget and oversee the deployment of large-scale projects across multiple sites internationally. In this role, you will guide large machine learning (ML) models for recommender systems. You would be expected to have solid expertise in machine learning technology and continue to develop and innovate in the areas of ranking, retrieval and large language models (LLM). Building such models, analyzing user data for gaps and opportunities and evaluation of model architectures at the intersection of model/hardware/infrastructure co-design for performance and efficiency are likely activities. You would likely focus on a workstream Google calls Large Recommender Models, involving using Semantic IDs and leveraging Gemini-based LLM models for especially difficult recommendation use cases. You would be involved in aligning research directions with cross-YouTube and cross-product area partners, and manage a small team. 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: $262000 - $364000 (USD) + 25% bonus target + equity + benefits Learn more about benefits at Google . Set and communicate team priorities that support the broader organization's goals. Align strategy, processes, and decision-making across teams. Set clear expectations with individuals based on their level and role and aligned to the broader organization's goals. Meet regularly with individuals to discuss performance and development and provide feedback and coaching. Develop the long-term technical goal and roadmap within, and often beyond, the scope of your teams. Evolve the roadmap to meet anticipated future requirements and infrastructure needs. Oversee systems designs within the scope of the broader area, and review product or system development code to solve ambiguous problems. Drive technical project strategy, lead large-scale ML infrastructure optimization, and oversee the design and implementation of advanced recommendation systems. Minimum qualifications: Bachelor's degree in Computer Science, a related technical field, or equivalent practical experience. 8 years of experience designing and building large-scale machine learning models. 8 years of experience building, testing, maintaining, and launching software products that require running and analyzing experiments and working with user data. 5 years of experience in a people management, supervision/team leadership role. Experience creating and aligning cross-organizational technical roadmaps across multiple software engineering, research, product management, and data science teams. Experience conducting research or applied research on recommender systems models or in related areas (e.g., ranking, retrieval, or user). Preferred qualifications: Master's degree or PhD in Computer Science, or a related technical field. Experience guiding research programs in relevant areas for small teams. Experience with hardware ML model ML, and framework co-design. Experience optimizing compute or hardware capacity. Publications in Recommender Systems, LLM’s, large model performance and efficiency.