About The Role

Number of Position(s): 1

Duration: 6 - 12 months
Dates: January 2027 - June 2027 - December 2027

Location: Onsite in San Jose, CA

Educational Requirements

Currently a candidate for a master's degree or PhD in computer science, software engineering, artificial intelligence / machine learning, computer engineering, electrical engineering, or a related field with an accredited school in the US.

What You Will Learn

This Co-Op position is an engineering-facing experience building AI agents for the optical test organization. You will gain practical knowledge in:

AI Agent Design: How to design, prompt, evaluate, and harden agent workflows (skills, tools, and guardrails) that engineers actually use in daily ODVT work.
Tooling and Integrations: Connecting agents to engineering systems - issue tracking, documentation, source control, and test data - so they fetch facts instead of inventing them.
Test Engineering Context: How Optical Design Verification Testing works: test plans, test cases and lab data
Software Craft: Python development, version control, structured documentation, tests, and iterating on tools from engineer feedback.
Fiber Communication Exposure: High-speed coherent optical products, DSP concepts, and how test data is used to judge product performance and reliability.
Design, implement, and refine AI agents that help ODVT engineers write test plans and test cases, analyze results, search documentation, and track work.
Build and maintain agent skills, prompts, and tool integrations (for example MCP servers and Python helpers) used by the team.
Improve reliability: reduce invented values, add source traceability, tests, and review gates so agent output is safe for lab and reporting use.
Partner with optical test engineers to identify high-value automation opportunities and turn them into working agent workflows.
Document how to use the agents, collect feedback, and measure whether the tools save time or improve quality.
Proficiency in Python (preferred); additional programming or scripting languages welcome.
Coursework or project experience with LLMs, AI agents, prompt engineering, retrieval-augmented generation (RAG), or similar.
Comfort with Git and collaborative software development.
Ability to write clear technical documentation and explain a technical design to non-software engineers.
Interest in applying AI to a real engineering team, with a skeptical eye toward correctness - not only academic demos.

Nice to have

Experience with Cursor, VS Code agent/copilot workflows, Model Context Protocol (MCP), or similar IDE-native AI tooling.
Familiarity with Jira, Confluence, and Git/Bitbucket (or GitHub).
Basic understanding of fiber optical communication systems, Digital Signal Processing (DSP), or lab/test data analysis.
Demonstrated attention to detail and strong problem-solving capabilities.
Ability to work effectively both independently and as part of a collaborative team.
Excellent written and verbal communication skills.