AI/ML Software Engineer, GenAI for Workspace

Google; United States; On-site; USD 147,000 - 210,000; Full time

Job summary

Google seeks a Software Engineer for AI/ML in Workspace, focused on generative AI features. You will collaborate across teams to improve model capabilities and product quality in Workspace, with exposure to Gemini collaboration and Google Cloud constraints.

The role emphasizes modeling, evaluation, experimentation, synthetic data, and model deployment within real products, contributing to end-to-end quality and scalability across Workspace offerings.

Qualifications

• Bachelor’s degree or equivalent practical experience.
• 2 years of software development experience in one or more programming languages, or 1 year of experience with an advanced degree.
• 1 year of experience with speech/audio, reinforcement learning, ML infrastructure, or ML field specialization.
• 1 year of experience with ML infrastructure (model deployment, evaluation, optimization, data processing, debugging).

Responsibilities

• Write product or system development code.
• Collaborate with peers and stakeholders through design and code reviews to ensure best practices amongst available technologies.
• 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.

Skills

Software development experience

ML experience

ML infrastructure

Education

Bachelor's degree or equivalent practical experience

Master's degree or PhD in CS or related field

Job description

Google seeks a Software Engineer for AI/ML in Workspace, focused on generative AI features. You will collaborate across teams to improve model capabilities and product quality in Workspace, with exposure to Gemini collaboration and Google Cloud constraints.

The role emphasizes modeling, evaluation, experimentation, synthetic data, and model deployment within real products, contributing to end-to-end quality and scalability across Workspace offerings.