Software Engineer III, Full Stack, Pixel Biometrics

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.
  
• 2 years of experience with data structures or algorithms.
  
• Experience with front end languages (e.g. JavaScript or TypeScript).
  

Preferred qualifications:

• Master's degree or PhD in Computer Science or related technical fields.
  
• Experience with backend development and distributed systems.
  

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 Google Pixel team focuses on designing and delivering the world's most helpful mobile experience. The team works on shaping the future of Pixel devices and services through some of the most advanced designs, techniques, products, and experiences in consumer electronics. This includes bringing together the best of Google's artificial intelligence, software, and hardware to build global smartphones and create transformative experiences for users across the world.

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).
  
• Build a web platform for teams to build visualizations of their machine learning (ML) system failures.
  
• Build visualizations for collected data and tools for data collection proctors.