Software Engineer, AI/ML, Workspace

About the Role: Software Engineer, AI/ML, Workspace

As a Software Engineer specializing in AI/ML for Google Workspace, you will be instrumental in developing the next-generation technologies that impact billions of users. Google's commitment to handling information at massive scale extends beyond web search, continuously pushing the boundaries of technology. This role offers the unique opportunity to work on projects critical to Google’s needs, with flexibility to switch teams and projects as you grow. We seek versatile engineers who demonstrate leadership and enthusiastically tackle full-stack challenges, driving technological advancement.

You will focus on enhancing the quality of Generative AI-powered features within Workspace. This involves leveraging your expertise in modeling, evaluation, experimentation, synthetic data generation, and general improvement of GenAI in real-world products. Collaboration will be key, as you partner with multiple teams and functions across Workspace and the Gemini teams to integrate model capabilities and deliver quality improvements to various Workspace products.

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.

Responsibilities

• 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.

Key skills/competency

• Generative AI
• Machine Learning
• Software Development
• ML Infrastructure
• Model Evaluation
• Data Processing
• Python
• Algorithms
• Distributed Systems
• Collaboration