Minimum qualifications:- Bachelor's degree or equivalent practical experience.
- 2 years of experience with developing large-scale infrastructure, distributed systems or networks, or experience with compute technologies or storage.
- 2 years of experience with software development in one or more programming languages (e.g., Java, Python, C ).
- Experience working with one or more of the following: distributed systems, backend services, cloud infrastructure, data processing systems or building developer platforms or frameworks.
- Experience with AI Algorithms.
Preferred qualifications:- Master's degree or PhD in Computer Science or related technical fields.
- Experience with Spanner or large-scale Google infrastructure.
- Experience building APIs, platform primitives, or infrastructure consumed by other engineering teams.
- Experience designing safety-critical systems.
- Familiarity with agent/Large Language Model (LLM) orchestration frameworks, tool-use patterns, or workflow execution engines.
- Proficiency in Java.
About the jobIn this role, you will help build the agent ecosystem: the primitives, guardrails, and knowledge integrations that enable data agents to author and operate autonomously, including validating impact, safety, and reversibility before any change is applied. As a part of this, data engineering agents on Google Cloud earn autonomy, and practitioners ship production pipelines without babysitting the automation.
Building agents is table stakes. Making them safe to run unattended is not.
"Agents you don't have to watch."Individual pay is determined by factors including job-related skills, experience, and relevant education or training.
US: $147000 - $210000 (USD) 15% bonus target equity benefits
Learn more about benefits at Google .
Responsibilities- Write product or system development code.
- Participate in, or lead design reviews with peers and stakeholders to decide amongst available technologies.
- Review code developed by other developers and provide feedback to ensure best practices (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.
