Staff Software Engineering, YouTube ML Efficiency at YouTube

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. The YouTube Discovery Efficiency team is responsible for improving performance and extracting maximum efficiency for machine learning and AI workloads that powers YouTube. In this role, you will work at the intersection of modeling and efficiency by helping evolve YouTube's models for next TPU generations. 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 - $301000 (USD) + 20% bonus target + equity + benefits Learn more about benefits at Google . • Monitor the evolving landscape of recommendation systems, actively prototyping and benchmarking emerging modeling techniques to keep our infrastructure cutting-edge and efficient. • Enable next-generation model architectures and training procedures. • Scale experimentation capacity under our resource envelope. • Reduce complexity and fragmentation in the ML training and serving ecosystem by providing standardized, composable, and reusable solutions. • Reduce experimenter toil through introduction of automation frameworks for training, evaluation, and model serving. • Bachelor’s degree or equivalent practical experience. • 8 years of experience in software development. • 5 years of experience leading ML design and optimizing ML infrastructure (e.g., model deployment, model evaluation, data processing, debugging, fine tuning). • 3 years of building large-scale recommendation systems, Machine Learning (ML), ranking, or personalization. Minimum qualifications: • Bachelor’s degree or equivalent practical experience. • 8 years of experience in software development. • 5 years of experience leading ML design and optimizing ML infrastructure (e.g., model deployment, model evaluation, data processing, debugging, fine tuning). • 3 years of building large-scale recommendation systems, Machine Learning (ML), ranking, or personalization. Preferred qualifications: • Solid knowledge of ML models/algorithm design and implementation and their application to real-world problems. • Ability to collaborate effectively across teams and functions. • Strong problem solving and quantitative reasoning skills. • Solid communication skills.

Requirements

Must-haves

Individual pay is determined by factors including job-related skills, experience, and relevant education or training.
Learn more about benefits at Google .
Monitor the evolving landscape of recommendation systems, actively prototyping and benchmarking emerging modeling techniques to keep our infrastructure cutting-edge and efficient.
Enable next-generation model architectures and training procedures.
Scale experimentation capacity under our resource envelope.
Reduce complexity and fragmentation in the ML training and serving ecosystem by providing standardized, composable, and reusable solutions.
Reduce experimenter toil through introduction of automation frameworks for training, evaluation, and model serving.
Bachelor’s degree or equivalent practical experience.
8 years of experience in software development.
5 years of experience leading ML design and optimizing ML infrastructure (e.g., model deployment, model evaluation, data processing, debugging, fine tuning).
3 years of building large-scale recommendation systems, Machine Learning (ML), ranking, or personalization.
Bachelor’s degree or equivalent practical experience.
8 years of experience in software development.
5 years of experience leading ML design and optimizing ML infrastructure (e.g., model deployment, model evaluation, data processing, debugging, fine tuning).
3 years of building large-scale recommendation systems, Machine Learning (ML), ranking, or personalization.

Nice-to-haves

US: $207000 - $301000 (USD) + 20% bonus target + equity + benefits
Solid knowledge of ML models/algorithm design and implementation and their application to real-world problems.
Ability to collaborate effectively across teams and functions.
Strong problem solving and quantitative reasoning skills.
Solid communication skills.

Application prep

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

Keywords to mirror

machine learningaiml

Platform: google
Effort: medium