Staff Software Engineer, Machine Learning, YouTube Music - Google
New York, NY
About the Job
Minimum qualifications:
- Bachelor's degree or equivalent practical experience.
- 8 years of experience in software development, and with data structures/algorithms.
- 5 years of experience testing, and launching software products, and 3 years of experience with software design and architecture.
- 5 years of experience with machine learning algorithms and tools (e.g., TensorFlow), Recommender Systems, deep learning, or personalization.
- Experience with C++ or Python.
Preferred qualifications:
- Master’s degree or PhD in Engineering, Computer Science, or a related technical field.
- 3 years of experience in a technical leadership role leading project teams and setting technical direction.
- Knowledge of machine learning models with industry experience.
- Ability to communicate and collaborate to create a shared idea or purpose cross-team and cross-functionality.
- Excellent research and investigative skills.
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.
The Music Discovery team is responsible for the music recommendations experience and quality across all surfaces, and the team's mission is to cultivate and deliver music by creating positive listening experiences and guiding users to the right music for any moment.
We are looking for an exceptionally strong Staff ML Engineer who can help us take ranking quality for music recommendations across all YouTube surfaces to the next level. You will partner with leads and quality engineers in the team to evaluate and improve existing approaches, and collaborate across YouTube, Google Research and DeepMind to explore innovative music ranking and modeling solutions that power listening experiences and drive music engagement.
We are seeking target-driven and pragmatic candidates who deliver results while also advocating new ideas and out-of-the-box thinking.
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 $189,000-$284,000 + 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.
Responsibilities
- Write product or system development code.
- Participate in, or lead design reviews with peers and stakeholders to decide on available technologies.
- 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.
- 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.