Job description

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 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. 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 . • 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. • 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. 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.

Requirements

Must-haves

This role specializes in techniques—including LLMs, generative retrieval, and long-sequence modeling—to build foundational models for personalized retrieval and early-stage ranking.
Learn more about benefits at Google .
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.
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.
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.
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.

Nice-to-haves

US: $262000 - $364000 (USD) + 25% bonus target + equity + benefits
Master’s degree or PhD in Engineering, Computer Science, or a related technical field.

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

aimlmachine learning

Platform: google
Effort: medium