Software Engineer III, Machine Learning, Recommendations, YouTube

corporate_fareYouTubeplaceMountain View, CA, USA

Minimum qualifications:

• Bachelor’s degree or equivalent practical experience.
• 2 years of experience with software development in one or more programming languages, or 1 year of experience with an advanced degree in an industry setting.
• 2 years of experience with data structures or algorithms in either an academic or industry setting.
• 2 years of experience with machine learning algorithms and tools (e.g., TensorFlow), artificial intelligence.
  
• Experience developing algorithms (ranking, recommendations, prediction, search quality, personalization) and software that generates suggestions based on various input and output targets.
  
• Experience in programming with Python.
  

Preferred qualifications:

• Master's degree or PhD in Computer Science or a related technical field.
  
• 4 years of experience in numerical analysis, algorithms and data-mining.
  
• 2 years of experience with performance, large scale systems data analysis, visualization tools, or debugging.
  
• Experience in code and system health, diagnosis and resolution, and software test engineering.
  
• Experience with NLP or Computer Vision.
  

About the job

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. 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.

The US base salary range for this full-time position is $136,000-$200,000 + bonus + equity + benefits. Our salary ranges are determined by role, level, and location. The range displayed on each job posting reflects the minimum and maximum target salaries for the position across all US locations. 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

• Write product or system development code.
• Review code developed by other developers and provide feedback to ensure best practices (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.
• Build Machine Learning (ML) algorithms and solutions to empower immersive live recommendation and to grow immersive live ecosystems.
  
• Set up growth metrics and goals, do the analysis and research to identify the growth areas, design/develop content understanding algorithms, improve recommendation models.