Staff GenAI Systems Engineer: Docs & Workspace

Google; New York (NY); On-site; USD 207,000 - 300,000; Full time

Job summary

Google is seeking a senior software engineer to develop full-stack Generative AI features for Docs and other Workspace tools. You will lead ML design and optimize AI infrastructure, deploy models, and evaluate GenAI quality while collaborating across functions to deliver high-impact AI-enabled products.

The role emphasizes building scalable, user-centric AI features with robust testing, deployment, and performance evaluations in a fast-paced environment.

Qualifications

• Bachelor's degree or equivalent practical experience.
• 8 years of experience in software development.
• 5 years of experience leading ML design and optimizing ML infrastructure.
• 5 years of experience testing, and launching software products.
• 2 years of experience with GenAI techniques (e.g., LLMs, Multi-Modal, Large Vision Models) or GenAI concepts.
• Experience in building Generative AI applications, Agentic workflows and GenAI quality evaluation.

Responsibilities

• Conduct evaluations and assessments for Generative AI models to build high quality AI features in Docs and Editors.
• Develop machine learning systems and full stack client-server features.
• Design and implement exciting features that are high quality and provide real user benefit.
• Balance experimentation with delivering real user value as we develop our Generative AI capabilities.

Skills

ML design

ML infra

GenAI

Software testing

Generative AI applications

Agentic workflows

Quality evaluation

Team leadership

Education

Bachelor's degree or equivalent practical experience

Master's degree or PhD in Engineering, Computer Science, or related field

Tools

TensorFlow

Job description

Google is seeking a senior software engineer to develop full-stack Generative AI features for Docs and other Workspace tools. You will lead ML design and optimize AI infrastructure, deploy models, and evaluate GenAI quality while collaborating across functions to deliver high-impact AI-enabled products.

The role emphasizes building scalable, user-centric AI features with robust testing, deployment, and performance evaluations in a fast-paced environment.