Semiconductor Data Science and Machine Learning Engineer

Keysight Technologies, Inc.•Santa Rosa, CA

34d•Onsite

About The Position

Keysight Technologies, the world's premier measurement company, has an opening for a Semiconductor Data Analytics and Machine Learning Engineer at the High Frequency Technology Center (HFTC) in Santa Rosa, California. Live in beautiful Sonoma County amongst vineyards, redwood forests, rivers, lakes and coastline while working on state-of-the-art ICs and devices. HFTC invents and delivers world-leading compound semiconductor and integration technology in our fabrication facilities located in Santa Rosa. Our team enables Keysight product leadership through semiconductor innovation for differentiated performance.

Requirements

Bachelor’s, Master’s, or Ph.D. in Data Science, Electrical Engineering, Computer Science, Materials Science, Applied Physics, or related fields.
5+ years of experience developing and deploying machine learning models using ONNX for classification, pattern recognition, anomaly detection, or related tasks (MS with 2-3+ years/PhD with 1+ years of applicable experience).
At least 3 years of experience in semiconductor manufacturing, processes or integration engineering.
Ability to work with cross-functional technical teams, interfacing with process and integration teams.
Strong communication and collaboration skills are required.
This role requires an in-person, hands-on engineer who can physically work with the test systems in Santa Rosa, CA.
Candidates must be a U.S. citizen or lawful permanent resident of the U.S., or protected individual, having authorization from the U.S. government for export-controlled items under 8 U.S.C. 1324b(a)(3).

Responsibilities

Develop semiconductor-related automated vision systems and big data projects.
Improve yield, improve process control, and increase operational efficiency.
Develop and refine machine learning models, analyze complex datasets, and apply deep semiconductor process knowledge to enable smart manufacturing goals.
Collaborate with cross-functional teams to build scalable data systems and analytical tools that enhance defect analysis, streamline data review, and strengthen feedback loops across HFTC.
Lead cross-functional engagement with semiconductor fab process engineering and integration teams to align data insights, clarify process requirements, and ensure model yield framework outputs effectively support decision-making.
Develop, train, and continuously refine machine learning models using optical inspection data to support yield determination, defect classification, and timely process feedback.
Build scalable data pipelines, tooling, and analytical frameworks capable of supporting complex defect analysis and high-volume data processing.
Apply deep semiconductor process knowledge to validate model assumptions, anticipate edge cases, and ensure meaningful interpretation of data in its physical process context.
Communicate findings, process implications, and recommended actions clearly to stakeholders ranging from peers to leadership.