ML Intern – Anomaly Detection
About The Position
Keysight is at 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 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.
Requirements
- Pursuing PhD in EE, Applied Math, CS, or related field.
- Hands-on experience with anomaly detection: unsupervised ML, autoencoders, normalizing flows, or contrastive/self-supervised learning.
- Strong background in Vision Models, Transformers, or GNNs applied to measurement, signal processing, or hardware data.
- Experience with generative models (GANs, VAEs, Diffusion) for low-data-regime augmentation.
- Background in Bayesian optimization or SPC for measurement or test optimization.
- Proficiency in Python, C++, CUDA; HPC/cloud training experience.
- Candidates who wish to be considered must be enrolled in a accredited college/university as of September 2026. Applicants who have graduated before September 2026 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.
Nice To Haves
- Experience applying anomaly detection to RF/microwave measurements, semiconductor test, or EDA data.
- Background in DoE, measurement uncertainty analysis, or test coverage optimization.
- Familiarity with Keysight measurement platforms or simulation tools (ADS, RFPro, EMPro).
- Publications or patents in anomaly detection, measurement ML, or NPI optimization.
Responsibilities
- Partner with NPI, product engineering, and RF measurement teams to define anomaly signatures, measurement quality metrics, and optimization objectives.
- Design and implement anomaly detection models: Vision Models (CNNs, ViTs) for spectrogram/field-based detection, Transformers for sequential measurement data, and unsupervised/semi-supervised methods (autoencoders, normalizing flows, contrastive learning) for out-of-distribution detection.
- Apply Bayesian and GP optimization for adaptive test sequencing, measurement coverage optimization, and test time reduction.
- Build generative models (GANs, Diffusion, VAEs) for anomaly data augmentation in low-shot NPI environments.
- Develop scalable ML pipelines for streaming measurement data with real-time inference for automated flagging and root-cause attribution.
- Benchmark anomaly detection models against ground-truth NPI characterization datasets for precision, recall, and false-alarm rate.
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What This Job Offers
Career Level
Intern
Education Level
Ph.D. or professional degree
