Scientific Machine Learning Intern Full-time

Overview

Keysight is on the forefront of technology innovation, delivering breakthroughs and trusted insights in electronic design, simulation, prototyping, test, manufacturing, and optimization. Our ~15,000 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 powerful, award-winning culture embraces a bold vision of where technology can take us and a passion for tackling challenging problems with industry-first solutions. Diversity, equity & inclusion are integral parts of our culture and drivers of innovation at Keysight. We believe that when people feel a sense of belonging, they can be more creative, innovative, and thrive at all points in their careers.

Responsibilities

We are seeking a highly motivated and qualified individual to join our team as a Machine Learning R&D (SciML) Intern. This role involves cutting-edge research and development in machine learning within the testing and measurement industry, with a focus on enabling next-generation simulation and design.

The successful candidate will develop and refine neural surrogate models for circuit, electromagnetic, and electrothermal simulations while implementing advanced techniques such as transformers, neural operators, mamba, and graph neural networks. They will design and test machine learning learning frameworks for complex simulation tasks and ensure the accuracy and reliability of AI models in scientific and engineering applications.

This role requires working closely with cross-functional teams to seamlessly integrate AI and ML solutions into Keysight’s products and business operations. The position demands a proactive and self-motivated individual who thrives in dynamic environments, quickly building relationships and adapting to change and ambiguity. Strong programming skills and experience in developing, prototyping, and delivering sophisticated algorithmic solutions are essential.

The ideal candidate will have a strong academic and practical background in deep learning, programming, and applied AI techniques. Hands-on experience with hardware-in-the-loop systems or physical simulation environments is highly desirable. A passion for interdisciplinary collaboration, integrating machine learning with engineering and testing, and solving real-world challenges is essential for success in this role

Qualifications

Candidates who wish to be considered must be enrolled in a accredited college/university as of September 2025. Applicants who have graduated before September 2025 will not be considered unless they are entering/applying to a MS or PHD program after graduating.

Visa Sponsorship is not available for this position. Candidates who now or at any point in the future require sponsorship for employment visa status (e.g., H-1B Visa status) may not be considered.

California Pay Range: $36.81-$42.47 an Hour

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.

Careers Privacy Statement

***Keysight is an Equal Opportunity Employer.***

Keysight Technologies Inc. is an equal opportunity employer. Qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, protected veteran status, disability or any other protected categories under all applicable laws.

• Education:
  Candidates should be currently pursuing a Ph.D. in Computer Science, Electrical Engineering, Applied Mathematics, or related fields. Master’s students with extensive project or work experience will also be considered.
• Programming Proficiency:
  Applicants should have strong skills in Python, C++, and CUDA.
• Experience with Machine Learning Frameworks:
  Proficiency in PyTorch, TensorFlow, Keras, and ONNX is required.
• Expertise in Key Areas:
  Applicants should have a strong understanding of the following areas:
  • Large Language Models (LLMs)
  • Transformers
  • Natural Language Processing (NLP)
  • Time-series prediction
  • Data analytics
  • Deep learning theory and advanced analytics
  • Differential equations
  • Neural Operators and PINNs (Physics-Informed Neural Networks)
  • Graph Neural Networks (GNNs)
  • Generative techniques, such as GANs and diffusion models
  • Reinforcement learning (RL)
  • Familiarity with scientific computing