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Forward Deployed Engineering Intern, BS/MS, Summer 2027

Google
Sunnyvale, USA
Intern
Onsite
USD 98000-131000 / year
Discovered 1 weeks ago
gcppython

Full Job Posting

Responsibilities

  • 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.

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