Role Overview
You will design, train, and evaluate neural surrogate models that predict engineering responses from simulation or measurement data far faster than repeated high-fidelity simulation or physical characterization. This spans the full arc of the problem: generating or assembling the data, choosing and evaluating model architectures and output representations, establishing where accuracy holds and where it degrades, and quantifying the result against a realistic simulation or measurement budget.
In the second phase you will study how a trained model can be specialized efficiently when the design problem shifts, using LoRA and related parameter-efficient adaptation methods. You will work alongside AI researchers, simulation and measurement experts, RF designers, and runtime engineers.
Learning Outcomes
Apply modern machine learning to realistic engineering simulation and measurement data.
Learn how surrogate model accuracy depends on data quality, sampling strategy, representation choices, and simulation budget.
Develop practical experience with parameter-efficient adaptation methods such as LoRA under data-scarce engineering conditions.
Build fluency with uncertainty estimation, physical consistency checks, and engineering-relevant error analysis.
Take a research model from Python experimentation toward robust C++ and LibTorch integration.
Collaborate across machine learning, RF design, simulation, measurement, and runtime engineering disciplines.
Responsibilities
Generate parametric engineering datasets using simulation and/or measurement sources, with attention to sampling strategy and to numerical noise in the underlying data.
Design, train, and evaluate neural surrogate models for engineering response prediction.
Investigate how the choice of output representation affects accuracy, particularly across operating regions or frequency ranges where the response varies sharply.
Establish rigorous baselines and quantify accuracy as a function of simulation or measurement budget.
Evaluate LoRA and related parameter-efficient methods for specializing trained models to new problem settings, against full and partial fine-tuning.
Develop calibrated uncertainty estimates so that predictions can be trusted or deferred appropriately.
Verify physical consistency and engineering constraints - including passivity, causality, and reciprocity where applicable - and report engineering-relevant metrics including worst-case behavior.
Analyze the simulation corpus and model behavior: parameter sensitivity, response dimensionality, data quality and solver noise, and where prediction error concentrates across frequency and design space.
Prototype model training and inference integration in C++ and LibTorch within an engineering ML runtime environment.
Qualifications
Required Qualifications
Currently pursuing or recently completed a PhD in Machine Learning, Computer Science, Applied Mathematics, Electrical or Electronic Engineering, or a related technical field.
Hands-on experience across multiple neural architectures - transformers, neural operators, graph networks, CNNs - and sound judgment about which suits a given problem.
Strong Python and PyTorch, with experience training and debugging neural networks well beyond tutorial scale.
Strong C++ and hands-on LibTorch experience to implement and integrate models into a production runtime.
Solid grasp of transfer learning, fine-tuning, and parameter-efficient methods such as LoRA.
Strong applied statistics and exploratory data analysis, including sensitivity analysis and dimensionality reduction, and the judgment to diagnose a model from its errors rather than its aggregate score.
Experimental discipline: rigorous baselines, careful ablations, experiment tracking, reproducible results.
Willingness to learn the underlying physics rather than treat it as arbitrary numerical data. Mentorship will be provided.
Preferred Qualifications
Background in RF, microwave, high-speed digital, or test and measurement engineering; familiarity with S-parameters, filters, or interconnect.
Experience with EM simulation tools and their scripting interfaces, such as Keysight ADS, RFPro, Momentum, or comparable tools, or engineering measurement and test automation environments.
Experience with surrogate modeling, operator learning, physics-informed machine learning, or scientific machine learning generally.
Experience with uncertainty quantification, conformal prediction, or Gaussian process regression.
CUDA, cuDNN, or TensorRT experience, or prior experience shipping a research model in C++.
Prerequisites
Version control, modular code structure, and reproducible experiment pipelines.
CMake and Git workflows for sizeable C++ projects, and comfort profiling and debugging C++ code.
Ability to design an evaluation that answers a question rather than one that produces a favorable number.
Clear technical communication to both machine learning researchers and engineering stakeholders.
What This Internship Offers
Apply modern machine learning to physical engineering data in an industrial setting.
Learn parameter-efficient adaptation and transfer learning under severe data scarcity.
Develop working fluency in RF and high-speed concepts, measurement data, and simulation workflows.
Take a research model the full distance, from Python prototype to robust C++.
The opportunity to shape how machine learning accelerates engineering design and test at a company that builds both the simulators and the instruments that validate them.
Mentorship across machine learning, simulation, measurement, and RF design, with access to engineering tools and realistic engineering datasets.
A portfolio-defining project spanning scientific machine learning, efficient model adaptation, and real-world engineering applications.
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.***
Generate parametric engineering datasets using simulation and/or measurement sources, with attention to sampling strategy and to numerical noise in the underlying data.
Design, train, and evaluate neural surrogate models for engineering response prediction.
Investigate how the choice of output representation affects accuracy, particularly across operating regions or frequency ranges where the response varies sharply.
Establish rigorous baselines and quantify accuracy as a function of simulation or measurement budget.
Evaluate LoRA and related parameter-efficient methods for specializing trained models to new problem settings, against full and partial fine-tuning.
Develop calibrated uncertainty estimates so that predictions can be trusted or deferred appropriately.
Verify physical consistency and engineering constraints - including passivity, causality, and reciprocity where applicable - and report engineering-relevant metrics including worst-case behavior.
Analyze the simulation corpus and model behavior: parameter sensitivity, response dimensionality, data quality and solver noise, and where prediction error concentrates across frequency and design space.
Prototype model training and inference integration in C++ and LibTorch within an engineering ML runtime environment.
Required Qualifications
Currently pursuing or recently completed a PhD in Machine Learning, Computer Science, Applied Mathematics, Electrical or Electronic Engineering, or a related technical field.
Hands-on experience across multiple neural architectures - transformers, neural operators, graph networks, CNNs - and sound judgment about which suits a given problem.
Strong Python and PyTorch, with experience training and debugging neural networks well beyond tutorial scale.
Strong C++ and hands-on LibTorch experience to implement and integrate models into a production runtime.
Solid grasp of transfer learning, fine-tuning, and parameter-efficient methods such as LoRA.
Strong applied statistics and exploratory data analysis, including sensitivity analysis and dimensionality reduction, and the judgment to diagnose a model from its errors rather than its aggregate score.
Experimental discipline: rigorous baselines, careful ablations, experiment tracking, reproducible results.
Willingness to learn the underlying physics rather than treat it as arbitrary numerical data. Mentorship will be provided.
Preferred Qualifications
Background in RF, microwave, high-speed digital, or test and measurement engineering; familiarity with S-parameters, filters, or interconnect.
Experience with EM simulation tools and their scripting interfaces, such as Keysight ADS, RFPro, Momentum, or comparable tools, or engineering measurement and test automation environments.
Experience with surrogate modeling, operator learning, physics-informed machine learning, or scientific machine learning generally.
Experience with uncertainty quantification, conformal prediction, or Gaussian process regression.
CUDA, cuDNN, or TensorRT experience, or prior experience shipping a research model in C++.
Prerequisites
Version control, modular code structure, and reproducible experiment pipelines.
CMake and Git workflows for sizeable C++ projects, and comfort profiling and debugging C++ code.
Ability to design an evaluation that answers a question rather than one that produces a favorable number.
Clear technical communication to both machine learning researchers and engineering stakeholders.
What This Internship Offers
Apply modern machine learning to physical engineering data in an industrial setting.
Learn parameter-efficient adaptation and transfer learning under severe data scarcity.
Develop working fluency in RF and high-speed concepts, measurement data, and simulation workflows.
Take a research model the full distance, from Python prototype to robust C++.
The opportunity to shape how machine learning accelerates engineering design and test at a company that builds both the simulators and the instruments that validate them.
Mentorship across machine learning, simulation, measurement, and RF design, with access to engineering tools and realistic engineering datasets.
A portfolio-defining project spanning scientific machine learning, efficient model adaptation, and real-world engineering applications.
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.***
