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Lead Test Engineer
LEIDOS, INC.
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Leidos is seeking a Test Engineer to join the Air Traffic Business Area within the Homeland Sector, supporting the development of the Leidos Common Automation Platform (L-CAP). L-CAP is a…
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Data Engineer
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Senior Embedded Software Engineer - Platform Software
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NEW JOB TEMPLATE
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Test & Evaluation Engineer
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Systems Integration Engineer
ENSCO, Inc.
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Senior Systems Engineer
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We are seeking a Senior Systems Engineer to work directly with Government mission leaders and independently governed program teams delivering elements of a critical space mission…
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AI Engineer - Graduate
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Thales is looking for a AI Engineer Graduate to join our engineering team and support the development of applied AI solutions that enhance the inflight passenger experience for millions of…
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Systems Engineer (Entry Level)
Technology for Energy Corporation
Knoxville, TN
…seeking a motivated and technically curious Entry-Level Systems Engineer to support the design, development, integration, testing, and support of technical products used in the aviation…
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Senior Python Engineer – Space Modeling and Simulation
The MITRE Corporation
Bedford, MA · Colorado Springs, CO · McLean, VA
…that operates federally funded R&D centers to deliver deep technical and systems integration, rapid prototype development, and mission-driven innovation in the public interest. Headquarters…
Added 9 mins ago
Power Systems Project Engineer
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Twin Disc is seeking an experienced Power Systems Project Engineer to lead the design, development, and integration of advanced high-voltage power electronics systems supporting…
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Agentic AI - PhD Intern (Modeling Agent)
Keysight Technologies, Inc.
Calabasas, CA · Santa Rosa, CA
…it, and explains both its choices and its results back to the…
Added 9 mins ago
Machine Learning - PhD Intern (Modeling and Data Analysis)
Keysight Technologies, Inc.
Calabasas, CA · Santa Rosa, CA
…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…
Added 11 mins ago
AB
Systems Engineer - HPC and GPU Infrastructure
Absolute Business Solutions Corp
Bethesda, MD
- $90,000 to $170,000 USD / year
ABSC has an exciting opening for a TS/SCI-cleared Systems Engineer specializing in high-performance computing and GPU infrastructure supporting DIA-NDOC under the DTP contract. You will…
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DevOps Platform Engineer and Linux Systems Administrator
Absolute Business Solutions Corp
Bethesda, MD
ABSC has an exciting opening for a TS/SCI-cleared DevOps Platform Engineer and Linux Systems Administrator supporting DIA-NDOC’s D3P modernization under the DTP contract. You will operate…
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Senior Product Owner
Absolute Business Solutions Corp
Bethesda, MD
- $135,000 to $245,000 USD / year
Manage, direct, coordinate, and provide technical direction for research, development, integration, deployment, and sustainment of NDOC mission applications.
Added 11 mins ago
AB
Product Owner
Absolute Business Solutions Corp
Bethesda, MD
- $110,000 to $225,000 USD / year
Manage, direct, coordinate, and provide technical direction for research, development, integration, deployment, and sustainment of NDOC mission applications.
Added 12 mins ago
Propulsion Engineer, Sr Staff - SRM / Solid Rocket Motors - Remote Option
Lockheed Martin
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Added 12 mins ago
Middleware Team Lead w/Secret Clearance
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Working knowledge of DoD Security Technical Implementation Guides (STIGs) and Information Assurance Vulnerability Management (IAVM).

Keysight Technologies, Inc.

Machine Learning - PhD Intern (Modeling and Data Analysis)

Calabasas, CA, United States
Santa Rosa, CA, United States

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Job details

Apply by: Nov 6, 2027
Posting language: English
Posted: Nov 6, 2026

Original job description

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.***

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More about this job

Skills

Machine learning
Data analysis
Estimating
Research
Python
C++
ML
Computer science
PyTorch
Debugging
Statistics
Test automation
CMake
Git
Communication skills

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