Software Engineer, AI/ML, Workspace

Write 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.Implement solutions in one or more specialized ML areas, utilize ML infrastructure, and contribute to model optimization and data processing.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.1 year 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.1 year 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 fields.2 years of experience with data structures and algorithms.Experience developing accessible technologies.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. In this role, you will work on different areas of quality for the generative AI-powered features in Workspace. You will have experience in modeling, evaluation, experimentation, synthetic data generation, and improvement of GenAI in real products. You will be collaborating across multiple teams and functions within Workspace as well as Gemini teams to bring model capabilities and quality improvements to various Workspace products.Google Cloud accelerates every organization’s ability to digitally transform its business and industry. We deliver enterprise-grade solutions that leverage Google’s cutting-edge technology, and tools that help developers build more sustainably. Customers in more than 200 countries and territories turn to Google Cloud as their trusted partner to enable growth and solve their most critical business problems.Individual pay is determined by factors including job-related skills, experience, and relevant education or training. US: $147000 - $210000 (USD) + 15% bonus target + equity + benefitsLearn more about benefits at Google.Note: By applying to this position you will have an opportunity to share your preferred working location from the following: Sunnyvale, CA, USA; New York, NY, USA.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.1 year 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.1 year of experience with ML infrastructure (e.g., model deployment, model evaluation, optimization, data processing, debugging).