Google's YouTube Ads team needs to build a centralized, scalable, and real-time platform for brand understanding in YouTube videos by leveraging multi-modal Gemini models at YouTube scale. The issue is to leverage AI techniques at a very large-scale with limited resources requiring innovation in modeling infrastructure.
Requirements
- 2 years of experience with developing large-scale infrastructure, distributed systems or networks, or with compute technologies, storage or hardware architecture.
- 2 years of experience in programming in C++ and Python, software engineering, infrastructure design, and machine learning infrastructure.
- 2 years of experience with data structures or algorithms in either an academic or industry setting.
- 2 years of experience with performance, large-scale systems data analysis, visualization tools, or debugging.
- Experience developing accessible technologies.
- Experience in building large-scale machine learning or AI systems for real world applications.
- Proficiency in code and system health, diagnosis and resolution, and software test engineering.
Responsibilities
- Write product or system development code.
- Build modeling infrastructure to engineer high performance solutions at YouTube scale, specifically large-scale Machine Learning solutions for solving video understanding problems that impact many new and critical applications in YT Ads.
- Design, build, and maintain the infrastructure for our real-time brand annotation platform.
- Review code developed by other developers and provide feedback to ensure best practices (e.g., style guidelines, checking code in, accuracy, testability, and efficiency).
- 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.
- Lead collaborations with client teams, VIA infrastructure, ML research teams, and more.
Other
- Bachelor’s degree or equivalent practical experience.
- Master's degree or PhD in Computer Science or related technical fields.
- 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.
- Contribute to existing documentation or educational content and adapt content based on product/program updates and user feedback.
- Google is proud to be an equal opportunity workplace and is an affirmative action employer.
