We are seeking an R&D engineer with a strong experimental-science background to advance eProbe semiconductor inspection tools through hands-on characterization, data analysis, and algorithm development. This is not an IT, infrastructure, or enterprise systems role. Work is hands-on in the lab and analytical at the desk: scopes and tool logs one day, coding and algorithm design the next. You will collaborate with hardware, FPGA/firmware, software, and applications engineers, and present clear recommendations grounded in data and physical reasoning.

The ideal candidate has a Ph.D. (or equivalent research depth) in Physics, Applied Physics, or Materials Science, enjoys ambiguous R&D problems, designs clean experiments, and can drive an investigation from hypothesis through measured result to an implementable algorithm or process change.

• Design and execute experiments to characterize and improve semiconductor inspection tool performance
• Investigate complex measurement-system behavior using a first-principles / physical-reasoning approach
• Develop and prototype algorithms in Python based on experimental data and engineering models
• Analyze large technical datasets to quantify system behavior, uncertainty, and improvement opportunities
• Translate R&D findings into clear recommendations for product, process, or algorithm changes
• Document experimental methods and results for internal reuse and technical review
• Present findings to cross-functional teams; support customer-facing technical discussions as needed
• Partner with hardware, firmware, software, and applications engineering to validate ideas on real tools
• Contribute to continuous improvement of characterization methods, analysis workflows, and tool capabilities
• Help triage and root-cause challenging technical issues that require experimental and analytical depth

• PHD. in Physics, Applied Physics, Materials Science, or related field (strong research Master’s considered)
• Proven ability to design original experiments, analyze data, and draw defensible conclusions
• Strong intuition for measurement systems: noise, dynamics, stability, and systematic error
• 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
• Strong oral and written communication skills

Nice to Have

• Experience with electron microscopes (SEM, TEM) or other ebeam or semiconductor test equipment
• Semiconductor process or cleanroom exposure
• Signal or image processing for scientific / metrology data
• MATLAB, Spotfire, Exensio, or similar analysis tools
• Some exposure to HDL (Verilog, System Verilog, VHDL)