Staff Applied ML Software Engineer, Recommendations, YouTube

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.

YouTube recommendation systems suggests videos that maximizes value to our viewers. We have cutting edge technology and ML based algorithms to retrieve and rank the best possible videos given the user context and preferences. Our recommendation stack is constantly evolving to keep up with the newest advancements in ML technology (e.g., transformers, foundation, Gemini models).

In this role, you will require very deep knowledge of ML technologies (especially in the area of recommendations). You will be working on cutting edge algorithms and optimization techniques. You will also require prior ML experience. You will have a research background in the form of a PhD in ML or publications in ML or related technical fields (including Physics, Math, Operations Research, Optimization, etc.).

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

• 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 recommendation systems, optimize ML infrastructure, and guide the development of model architecture.
  
• Be comfortable analyzing data, drawing conclusions from the logs and using ML to improve the product.

Minimum qualifications:

• Bachelor's degree in Computer Science or a related field, or equivalent practical experience.
• 8 years of experience in software development.
  
• 5 years of experience building and deploying recommendation systems models (retrieval, prediction, ranking, embedding) in production, and experience building architecture in different modeling domains.
  
• 5 years of experience with ML design and ML infrastructure (e.g., model deployment, model evaluation, data processing, debugging, fine tuning).
• 3 years of experience in Applied Machine Learning.
• Experience with publications in ML, or a related field.

Preferred qualifications:

• Master's degree or PhD in Computer Science or related field.
• 8 years of experience with data structures and algorithms.
• 3 years of experience in a technical leadership role leading project teams and setting technical direction.
  
• 3 years of experience working in a complex, matrixed organization involving cross-functional, or cross-business projects.

In most instances, this position requires in-person interviews as part of the hiring process.