# Machine Learning PhD Intern - Modeling and Data Analysis at Keysight Technologies

- Company: [Keysight Technologies](https://keysight.com)
- Location: Calabasas, CA
- Type: Internship · Internship
- Posted: 2026-11-06
- Apply: https://jobs.keysight.com/jobs/54742?icims=1&lang=en-us
- Page: https://newgrad.ai/jobs/machine-learning-phd-intern-modeling-and-data-analysis-4b45ecee

## Summary by newgrad.ai

What you'll do:

- Develop neural surrogate models to predict engineering responses from simulation and measurement data.
- Implement parameter-efficient adaptation methods like LoRA to specialize models for new design problems.
- Integrate research-based Python models into production-ready C++ and LibTorch engineering environments.
- Analyze model performance, uncertainty, and physical consistency across complex engineering datasets.

What they're looking for:

- Current PhD students in Machine Learning, Computer Science, Engineering, or Applied Mathematics.
- Proficiency in Python, PyTorch, C++, and LibTorch for neural network development.
- Experience with neural architectures, transfer learning, and rigorous experimental design.

Pay and perks:

- Hourly pay range for PhD interns is $51–$56.

## Posting

### Overview

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 Program

Keysight's AI team builds adaptive engineering intelligence systems where measurement, simulation, and machine learning converge - models that learn from complex physical data and adapt to new design and testing conditions.

This internship focuses on machine learning surrogate models for engineering simulation and measurement. High-fidelity simulations and physical measurements can dominate the design and test loop for RF, high-speed, and other engineering problems, and their cost limits how much of a design space an engineer can explore. You will develop models that predict engineering responses at a fraction of the cost, investigate how such models can be adapted efficiently as the design problem changes, and integrate the result into an engineering ML runtime environment.

### 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.*

From Keysight Technologies's public posting, laid out for reading by newgrad.ai with the wording unchanged. Check the original before you apply.

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