Individual pay is determined by factors including job-related skills, experience, and relevant education or training.
US: $98000 - $131000 (USD) + 0% bonus target
Learn more about benefits at Google.
Serve as a developer for complex AI applications, transitioning from rapid prototypes to production-grade agentic workflows (e.g., multi-agent systems, MCP servers) that drive measurable ROI.
Architect and code the connective tissue between Google’s AI products and customer's live infrastructure, including APIs, legacy data silos, and security perimeters as part of an expert team.
Build high-performance evaluation pipelines and observability frameworks to ensure agentic systems meet rigorous requirements for accuracy, safety, and latency.
Identify repeatable field patterns and friction points in Google’s AI stack, converting them into reusable modules or formal product feature requests for the Engineering teams.
Co-build with customer engineering teams to instill Google-grade development best practices, ensuring long-term project success and high end-user adoption.
Minimum qualifications
Currently pursuing a Bachelor's or Master's degree in Engineering, Computer Science, or a related technical field.
Experience with software development using Python or a similar coding languages.
Experience taking production-grade AI-driven solutions from conception to launch and architecting AI systems on cloud platforms (e.g., Google Cloud Platform (GCP)).
Experience building pipelines for structured and unstructured data using both vector databases and RAG-like architectures to power enterprise AI solutions.
Preferred qualifications
Currently attending a degree program in the US and available to work full time for 12 weeks outside of university term time.
In their penultimate academic year or returning to a degree program after completion of the internship.
Experience implementing multi-agent systems using frameworks (e.g., LangGraph, CrewAI, ADK) and complex patterns (e.g., ReAct, self-reflection, hierarchical delegation).
Experience leading technical discovery sessions.
Knowledge of Large Language Model (LLM) native metrics (e.g., tokens/sec, cost-per-request) and techniques for optimizing state management and granular tracing.
Currently pursuing a Bachelor's or Master's degree in Engineering, Computer Science, or a related technical field.
Experience with software development using Python or a similar coding languages.
Experience taking production-grade AI-driven solutions from conception to launch and architecting AI systems on cloud platforms (e.g., Google Cloud Platform (GCP)).
Experience building pipelines for structured and unstructured data using both vector databases and RAG-like architectures to power enterprise AI solutions.
