Staff Software Engineer, Quality, Google Cloud, Applied AI
Role details
Job location
Tech stack
Job description
- Build the core logic for multi-modal customer support agents that execute complex reasoning across sales and support.
- Develop automated systems and tools that allow agents to iteratively build, test, and refine other agents.
- Architect the pathways that embed Gemini and Vertex AI intelligence directly into client-facing Cloud infrastructure.
- Establish engineering benchmarks to replace manual "trial-and-error" testing with automated, high-fidelity optimization.
- Take ownership of AI quality for production systems by defining technical metrics aligned with business goals, implementing evaluation frameworks, designing experiments, analyzing loss patterns, and driving improvements through system changes or training data enhancements.
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.
Requirements
Do you have experience in Software engineering?, Do you have a Master's degree?, * Bachelor's degree or equivalent practical experience.
- 8 years of experience with software development in one or more programming languages (e.g. C++).
- Experience with Artificial Intelligence, Distributed Systems, LLMs and High Performance Computing., * Master's degree or PhD in Computer Science, or a related field with a focus on Systems or AI.
- Experience building and maintaining multi-agent systems or complex applications in an enterprise setting.
- Experience with evaluation frameworks for AI quality in production (A/B testing, shadow deployment, online learning).
- Experience with model evaluation and mitigation, context engineering, benchmarking, and testing agentic systems.
- Ability to navigate ambiguity and iterate quickly on customer feedback to deliver solutions that precisely meet market needs.