Senior Software Engineer, AI/ML Infrastructure, YouTube

Collaborate with peers and stakeholders through design and code reviews to ensure best practices amongst available technologies (e.g., style guidelines, checking code in, accuracy, testability, and efficiency).Contribute to existing documentation or educational content and adapt content based on product/program updates and user feedback.Triage product or system issues and debug/track/resolve by analyzing the sources of issues and the impact on hardware, network, or service operations and quality.Design and implement solutions in one or more specialized ML areas, leverage ML infrastructure, and demonstrate expertise in a chosen field.Write and test product or system development code.Minimum qualifications:Bachelor’s degree or equivalent practical experience.5 years of experience with programming in Python and C++.3 years of experience testing, maintaining, or launching software products, and 1 year of experience with software design and architecture.3 years of experience with ML infrastructure (e.g., model deployment, model evaluation, optimization, data processing, debugging).3 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.Preferred qualifications:Master's degree or PhD in Computer Science or related technical field.5 years of experience with data structures and algorithms.1 year of experience in a technical leadership role.Experience developing accessible technologies.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.As a Senior Software Engineer for ML Infrastructure at YouTube, you will directly envision, design, and lead implementation of new state-of-the-art capabilities in our JAX-based next-generation ML frameworks tailored to YouTube's recommender model training needs.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: $174000 - $252000 (USD) + 15% bonus target + equity + benefitsLearn more about benefits at Google.Bachelor’s degree or equivalent practical experience.5 years of experience with programming in Python and C++.3 years of experience testing, maintaining, or launching software products, and 1 year of experience with software design and architecture.3 years of experience with ML infrastructure (e.g., model deployment, model evaluation, optimization, data processing, debugging).3 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.