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.
About the jobThe 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.
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 .
Responsibilities - 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.
