Keysight is at the forefront of technology innovation, delivering breakthroughs and trusted insights in electronic design, simulation, prototyping, test, manufacturing, and optimization. Our ~16,800 employees create world-class solutions in communications, 5G, automotive, energy, quantum, aerospace, defense, and semiconductor markets for customers in over 100 countries. Learn more about what we do.

Our award-winning culture embraces a bold vision of where technology can take us and a passion for tackling challenging problems with industry-first solutions. We believe that when people feel a sense of belonging, they can be more creative, innovative, and thrive at all points in their careers.

About the role

Keysight's AI team builds adaptive engineering intelligence systems where measurement, simulation, and machine learning converge. This PhD internship focuses on the agentic layer that makes those systems usable for engineering teams.

You will design, build, and evaluate an agentic flow that takes a new use case from an engineering team and stands up a modeling and optimization workflow with minimal manual configuration. The agent analyzes available data, proposes and validates a setup, connects to approved tools needed to execute it, and explains both its choices and its results back to the engineer.

A central constraint is that the agent's output must be auditable and reproducible. Engineers need to see why a configuration was chosen, correct it, and re-run it deterministically. This includes building a benchmark of representative engineering use cases and establishing how reliably the flow completes bounded tasks with appropriate human oversight.

• Build an agentic flow that configures data preparation, model training, and optimization for a new use case with minimal input from the user.
• Develop tool integrations connecting the agent to simulation, data, optimization, and analysis systems.
• Implement autonomous data analysis so the agent can characterize an unfamiliar dataset and choose an appropriate setup.
• Build explainability into the flow, including why a configuration was chosen, what the resulting model depends on, and how confident it is.
• Design guardrails such as validation before expensive runs, bounded actions, and compute budgets so failures are cheap and visible.
• Assemble a benchmark of representative engineering use cases with objective success criteria, and measure task-completion reliability and result quality against an expert-configured baseline.
• Apply agentic AI to realistic engineering modeling and optimization workflows while developing practical experience evaluating non-deterministic systems.
• Collaborate across AI research, software engineering, RF design, and test and measurement domains.

Required Qualifications

• Currently pursuing or recently completed a PhD in Machine Learning, Computer Science, Electrical or Electronic Engineering, Applied Mathematics, or a related technical field, with experience building and evaluating agent systems.
• Strong Python and software engineering skills, including modular code, version control, and reproducible pipelines.
• Hands-on experience building LLM agents, including tool and function calling, orchestration, and context management beyond prompt-level experimentation.
• Demonstrated rigor in evaluating non-deterministic systems, including benchmark design, held-out test sets, automated scoring, and careful interpretation of results.
• A portfolio of agent or LLM systems you have built, with code and an explanation of how you evaluated them.
• Working knowledge of the machine learning workflow the agent must configure, including data preparation, model selection, training, and optimization.
• Sound judgment about autonomy: where an agent should act, where it should ask, and where it should refuse.
• Clear communication to both AI researchers and domain engineers.

Preferred Qualifications

• Industry experience shipping agent or LLM-based systems to real users.
• Experience with multi-agent systems or agent frameworks, and with MCP or comparable tool-integration protocols.
• Background in optimization, design of experiments, or surrogate modeling.
• Exposure to RF, microwave, or electromagnetic engineering problems.
• Experience with retrieval-augmented systems or structured generation.
• Familiarity with C++ for integration with internal engineering tooling.

What This Internship Offers

• Mentorship across agentic AI, software systems, and engineering workflows, with access to representative engineering use cases.
• A portfolio-defining project in applied agentic AI, evaluated on reliability and usefulness in representative engineering workflows.
• Build an AI system that works across engineering tools and supports multi-step engineering tasks.
• Learn to measure longer-running, non-deterministic systems rigorously, including reliability, recovery, and human intervention.
• Deliver an agentic workflow that people can understand, supervise, and use in a real engineering process.

CA Pay Range:

(PhD) $51-$56

Candidates who wish to be considered must be enrolled in an accredited college/university as of September 2027. 

Careers Privacy Statement***Keysight is an Equal Opportunity Employer.***