ML PhD Intern: Surrogate Modeling for Engineering

Keysight Technologies; Calabasas, Santa Rosa (CA, CA); On-site; USD 70,000 - 77,000; Full time

Benefits offered by this job

Mentorship across ML, simulation, and‑

Access to engineering tools and data

Job summary

Keysight Technologies is offering an internship focused on building neural surrogate models for RF engineering simulations and measurements. You will design, train, and evaluate models, study efficient adaptation with LoRA, and integrate results into a C++ LibTorch runtime.

You will collaborate with AI researchers, RF designers, and runtime engineers to accelerate engineering design and testing using data-driven surrogate models.

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.

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.

Skills

Python

PyTorch

C++

LibTorch

LoRA

Transfer learning

Education

PhD in ML/CS/EE

Tools

Keysight ADS

RFPro

Momentum

Job description

Keysight Technologies is offering an internship focused on building neural surrogate models for RF engineering simulations and measurements. You will design, train, and evaluate models, study efficient adaptation with LoRA, and integrate results into a C++ LibTorch runtime.

You will collaborate with AI researchers, RF designers, and runtime engineers to accelerate engineering design and testing using data-driven surrogate models.