Responsibilities
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.
Qualifications
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
Based on experience, education and skills, most offers will be between the minimum and the midpoint of the Salary Range listed above.
Note: For other locations, pay ranges will vary by region.
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.***
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
Based on experience, education and skills, most offers will be between the minimum and the midpoint of the Salary Range listed above.
Note: For other locations, pay ranges will vary by region.
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.***
