Staff Software Engineer, Machine Learning, Google Pixel Watch

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, or applied ML
  

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.
  
• 3 years of experience working in a complex, matrixed organization involving cross-functional, and/or cross-business projects.
  
• Experience using digital signal processing techniques, working with sensor data (e.g. accelerometer data), and writing and deploying features in C++.
• Experience optimizing and launching machine learning (ML) based features on consumer electronics products.
  
• Experience with real-time operating systems (e.g. Android).
  

Experience using digital signal processing techniques, working with sensor data (e.g. accelerometer data), and writing and deploying features in C++.Experience using digital signal processing techniques, working with sensor data (e.g. accelerometer data), and writing and deploying features in C++.

About the job

As a Staff Software Engineer, you will develop on-device machine learning algorithms for our next generation Pixel Watches. You will work with Product, UXD, UXR, Quality Assurance, and other engineering teams to ideate, create, and deliver high quality ML based experiences for our users. In addition to your direct contributions, you will serve as the technical lead for algorithm development efforts on the team, consulting and providing mentorship to other algorithm developers.

Google's mission is to organize the world's information and make it universally accessible and useful. Our Devices & Services team combines the best of Google AI, Software, and Hardware to create radically helpful experiences for users. We research, design, and develop new technologies and hardware to make our user's interaction with computing faster, seamless, and more powerful. Whether finding new ways to capture and sense the world around us, advancing form factors, or improving interaction methods, the Devices & Services team is making people's lives better through technology.

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.

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

• Develop user-facing features powered by ML algorithms. Experiment, fine tune, and deploy features that run on-device in low power, memory constrained environments.
• Work with UXR teams to define and collect user data, build and train ML models using various techniques, and evaluate their performance. Refine and iterate models based on feedback from product managers, user studies and feedback, and production.
• Optimize for the device and user. Understand the tradeoffs between algorithm accuracy, power, compute, memory, and latency, and make optimization decisions based on an understanding of the big picture.
• Serve as technical lead for algorithm development on the team. Guide and mentor other developers, and stay up to date with the latest advancements in machine learning techniques and tools and help grow the team's expertise. Develop a technical roadmap of new features and feature improvements.