Staff Engineer, Device Statistics & Model Correlation

About the role

As a member of the Link Design and Architecture team, you will own the statistics that connect device measurements to models across wafer, package, and system levels. You will turn measurement data into statistical descriptions of photonic devices, prepare inputs for compact models and link simulations, and track correlation between simulation models and full-system measurements.

What you'll do

• Work with the data infrastructure team to bring internal test data and foundry and vendor deliveries into an analysis-ready format with traceable data lineage.
• Build statistical descriptions of photonic devices by fitting distributions, separating lot-to-lot, wafer-to-wafer, die-to-die, and within-die variance, and estimating parameter correlations and covariance.
• Partner with photonic device engineers to select measured parameters and collaborate with link modeling engineers to express statistics for compact models and link simulations using Monte Carlo and corner inputs.
• Maintain correlation between full simulation models and full-system measurements across tapeouts and lots, tracking residuals to identify model versus test discrepancies.
• Contribute to the device specification-setting methodology, deriving parametric yield estimates and specification limits from measured distributions.
• Document methods, assumptions, and data lineage to ensure results are reproducible and reviewable.

What you'll need

• BS in Electrical Engineering, Physics, Optics/Photonics, Statistics, or a related field.
• 4+ years of experience analyzing measurement data for semiconductor or photonic devices.
• Strong applied statistics background including distribution fitting, variance decomposition, correlation and covariance estimation, regression, hypothesis testing, design of experiments, and Monte Carlo methods.
• Strong Python skills for scientific computing and data analysis using NumPy, SciPy, pandas, statsmodels, or similar libraries with large tabular datasets.
• Working knowledge of photonic device physics to interpret parameter shifts across modulators, photodetectors, grating couplers, and waveguides.

Nice to have

• MS or PhD in Electrical Engineering, Physics, Optics/Photonics, Statistics, or a related field.
• Prior experience with silicon photonics or foundry process-control data including WAT/PCM and JMP.
• Experience with statistical parameterization of compact models.
• Experience validating measurement systems and test methods, including gauge R&R and repeatability versus device variation.
• Experience with data pipelines and versioned datasets using Git, SQL or Parquet, and dashboards.
• Familiarity with Bayesian or hierarchical models for lot, wafer, and die structure.

Skills: compact models, photonic device physics, WAT/PCM, Monte Carlo, Python

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