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# Machine Learning - PhD Intern (Modeling and Data Analysis)

- **Company:** Keysight Technologies
- **Location:** Santa Rosa, CA, United States
- **Experience:** Internship
- **Salary:** $106,080.0 - $116,480.0
- **Contract:** Internship / Graduate position
- **Skills:** Artificial Intelligence, Data Analysis, Artificial Neural Networks, Automation of Tests, C++ (Programming Language), CMake, Nvidia CUDA, Software Debugging, Python (Programming Language), Machine Learning, Network Switches, Pytorch, Transfer Learning, Uncertainty Quantification (UQ), cuDNN, Code Structure, Gaussian, Git Flow, Information Technology, TensorRT, Software Version Control
- **Published:** November 8, 2026
- **Apply:** https://jobs.localjobnetwork.com/apply/add/88813034/1

## About the Role

* 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., * 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.

## Description

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.