Semiconductor R&D Engineer: Lab-to-Algorithm Innovator

PDF Solutions, Inc.; Milpitas (CA); On-site; USD 120,000 - 180,000; Full time

Job summary

PDF Solutions, Inc. in Milpitas, CA seeks an R&D engineer with a strong experimental-science background to advance eProbe semiconductor inspection tools. You’ll work hands-on in the lab and analytically at the desk, collaborating with hardware, FPGA/firmware, software, and applications engineers to drive data-grounded recommendations.

The ideal candidate holds a Ph.D. in Physics, Applied Physics, or Materials Science and can drive investigations from hypothesis through measurable results to

Qualifications

• PhD in Physics, Applied Physics, Materials Science, or related field (strong research Master’s considered).
• Ability to design original experiments, analyze data, and draw defensible conclusions.
• Hands‑on lab experience with precision instrumentation (oscilloscopes, cables, power).
• Python for scientific computing and data analysis; NumPy/Pandas; plotting stack.
• Ability to turn experimental data into models or algorithm prototypes.

Responsibilities

• Design and execute experiments to characterize and improve semiconductor inspection tool performance.
• Investigate measurement-system behavior using first-principles/physical reasoning.
• Develop and prototype algorithms in Python based on experimental data and models.
• Analyze large technical datasets to quantify system behavior, uncertainty, and improvements.

Skills

Python for scientific computing

NumPy/Pandas

Data analysis

Algorithms development

Education

PhD in Physics, Applied Physics, or Materials Science

Tools

MATLAB

HDL (Verilog/SystemVerilog)

Laboratory instrumentation

Job description

PDF Solutions, Inc. in Milpitas, CA seeks an R&D engineer with a strong experimental-science background to advance eProbe semiconductor inspection tools. You’ll work hands-on in the lab and analytically at the desk, collaborating with hardware, FPGA/firmware, software, and applications engineers to drive data-grounded recommendations.

The ideal candidate holds a Ph.D. in Physics, Applied Physics, or Materials Science and can drive investigations from hypothesis through measurable results to