Software Engineer III, AI/ML Recommendations, Rankings, Predictions, YouTube
Indexed description
- Bachelor’s degree or equivalent practical experience.
- 2 years of experience programming in Python and C++.
- 1 year of experience building and deploying recommendation systems models (retrieval, prediction, ranking, personalization, search quality, embedding) in production.
- 1 year of experience with ML infrastructure (e.g., model deployment, model evaluation, optimization, data processing, debugging).
- Master's degree or PhD in Computer Science or related technical fields.
- 2 years of experience with data structures or algorithms.
- Experience developing accessible technologies.
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: $147000 - $211000 (USD) + 15% bonus target + equity + benefits
Responsibilities
Learn more about benefits at Google .
- Write product or system development code.
- Collaborate with peers and stakeholders through design and code reviews to ensure best practices amongst available technologies (e.g., style guidelines, checking code in, accuracy, testability, and efficiency,)
- Contribute to existing documentation or educational content and adapt content based on product/program updates and user feedback.
- 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.
- Build and deploy recommendation systems models, utilize ML infrastructure, and contribute to model optimization and data processing.
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