Machine Learning Engineer

Keysight Technologies, Inc.•Santa Rosa, CA

13d

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 ~16,800 employees create world-class solutions in communications, 5G, automotive, energy, quantum, aerospace, defense, and semiconductor markets for customers in over 100 countries. 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 (RF/Microwave), Applied Physics, CS, or related field.
Hands-on RF measurement experience: S-parameter characterization, calibration, and de-embedding.
Strong ML background with experience applying GNNs, Transformers, or Neural Operators to measurement or signal processing tasks.
Background in Bayesian optimization applied to hardware calibration or measurement tuning.
Familiarity with SCPI/IVI/PyVISA instrument control and RF measurement automation.

Nice To Haves

Experience with advanced calibration techniques (multiline TRL, unknown thru, in-fixture, on-wafer probing).
Background in load-pull, noise figure, or linearity characterization (IP3, P1dB) for RF device optimization.
Familiarity with Keysight RF platforms (PNA, PNA-X, ENA) or simulation tools (ADS, RFPro).
Publications or patents in ML for RF measurement, smart calibration, or adaptive test.

Responsibilities

Partner with RF measurement and post-silicon teams to translate calibration workflows, de-embedding challenges, and characterization requirements into ML-ready formulations.
Develop ML models for adaptive calibration, intelligent de-embedding, and fixture characterization — including Transformers for frequency-domain signal analysis and Vision Models for calibration artifact identification.
Apply Bayesian/GP optimization for adaptive calibration loop closure, impedance tuner control, and parametric yield optimization; RL (PPO, DDPG, SAC) for automated measurement sequencing and closed-loop RF tuning.
Build generative models for synthetic S-parameter dataset generation and calibration standard augmentation.
Write production-ready Python (scikit-rf, PyTorch), C++, and CUDA code; integrate with instrument control frameworks (SCPI, IVI, VISA).
Benchmark models against hardware measurements and physics-based calibration standards.