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 jobGoogle'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.
The US base salary range for this full-time position is $207,000-$300,000 bonus equity benefits. Our salary ranges are determined by role, level, and location. Within the range, individual pay is determined by work location and additional factors, including job-related skills, experience, and relevant education or training. Your recruiter can share more about the specific salary range for your preferred location during the hiring process.
Please note that the compensation details listed in US role postings reflect the base salary only, and do not include bonus, equity, or 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.
