Senior Software Engineer, AI/ML, Google Workspace

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.

AI will change the future of work in profound ways, and our products— Gmail, Docs, Drive, Calendar, Sheets, Vids and Meet are at the forefront. From pre-computed summaries for email threads, summaries for meetings, and videos created from a document using lifelike AI avatars, our AI opportunity is huge. Our mission is to meaningfully connect people so they can create, build, and grow together and as part of the team you can build how productivity tools should work 5-10 years into the future. You will work with model builders (Google DeepMind), work with exceptional leaders, and have the ability to impact billions of users across the world.

Individual pay is determined by factors including job-related skills, experience, and relevant education or training.

US: $174000 - $252000 (USD) + 15% bonus target + equity + benefits

Learn more about benefits at Google.

Responsibilities

• Write and test 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.
  
• Design and implement solutions in one or more specialized ML areas, leverage ML infrastructure, and demonstrate expertise in a chosen field.
  

Minimum qualifications:

• Bachelor’s degree or equivalent practical experience.
  
• 5 years of experience programming in Python or C++.
• 3 years of experience testing, maintaining, or launching software products, and 1 year of experience with software design and architecture.
  
• 3 years of experience with one or more of the following: Speech/audio (e.g., technology duplicating and responding to the human voice), reinforcement learning (e.g., sequential decision making), ML infrastructure, or specialization in another ML field.
  
• 3 years of experience with ML infrastructure (e.g., model deployment, model evaluation, optimization, data processing, debugging).

Preferred qualifications:

• Master's degree or PhD in Computer Science or related technical field.
  
• 5 years of experience with data structures and algorithms.
• 1 year of experience in a technical leadership role.
  
• Experience developing accessible technologies.
  

• Bachelor’s degree or equivalent practical experience.
  
• 5 years of experience programming in Python or C++.
• 3 years of experience testing, maintaining, or launching software products, and 1 year of experience with software design and architecture.
  
• 3 years of experience with one or more of the following: Speech/audio (e.g., technology duplicating and responding to the human voice), reinforcement learning (e.g., sequential decision making), ML infrastructure, or specialization in another ML field.
  
• 3 years of experience with ML infrastructure (e.g., model deployment, model evaluation, optimization, data processing, debugging).

In most instances, this position requires in-person interviews as part of the hiring process.