Software Engineer, AI/ML, Workspace

Google

📍Sunnyvale, CA
Posted May 19, 2026

Job Overview

Position

Software Engineer, AI/ML, Workspace

Company

Google

Location

Sunnyvale, CA

Work Type

On-site

Job ID

li-4415791022

Job Description

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
.
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.

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.

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.

The US base salary range for this full-time position is $147,000-$211,000 + bonus + equity + benefits. Our salary ranges are determined by role, level, and location. Within the range, individual pay is determined by work location and additional factors, including job-related skills, experience, and relevant education or training. Your recruiter can share more about the specific salary range for your preferred location during the hiring process.

Please note that the compensation details listed in US role postings reflect the base salary only, and do not include bonus, equity, or benefits. Learn more about benefits at Google .

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.

Google is proud to be an equal opportunity workplace and is an affirmative action employer. We are committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity or Veteran status. We also consider qualified applicants regardless of criminal histories, consistent with legal requirements. See also Google's EEO Policy and EEO is the Law. If you have a disability or special need that requires accommodation, please let us know by completing our Accommodations for Applicants form .

Interview Prep

AI-powered insights to help you prepare

Key Skills

Required:
Preferred:

Practice Questions

💡Technical Questions (3)
  • 1.How would you design an evaluation pipeline to assess the quality and safety of a generative AI feature integrated into Google Workspace, such as a doc summarization tool?
  • 2.Can you describe your approach to debugging and optimizing a deployed ML model that is experiencing increased latency and degraded prediction quality in a production environment?
  • 3.Explain how you would use synthetic data generation to improve a GenAI feature for Workspace. What are the potential pitfalls and how would you mitigate them?
🎯Behavioral Questions (3)
  • 1.Tell me about a time you collaborated with cross-functional teams, such as researchers, product managers, or other engineering teams, to bring an ML model into production.
  • 2.Describe a situation where you had to debug a critical issue in an ML pipeline under time pressure. How did you handle it?
  • 3.Give an example of a time you advocated for accessibility in a product or feature you were developing.
🧩Situational Questions (2)
  • 1.You are tasked with improving the quality of a GenAI feature in Workspace, but the Gemini model team informs you that the base model cannot be retrained for another three months. What do you do?
  • 2.You notice that a newly deployed ML model for a Workspace feature is performing well on average, but is producing biased or unhelpful results for a specific subset of international users. How would you triage and resolve this?

Resume Keywords

Make sure these keywords appear on your resume

Generative AIML InfrastructureModel DeploymentModel EvaluationSynthetic DataWorkspaceAccessibilityData StructuresExperimentationDebuggingCross-functional CollaborationPython

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