Senior Applied Scientist – AI/ML Calibration & Optimization

To make electronics, you need chips, wafers, transistors, reticles, and... To make these, you must see, test and manufacture them at scale—faster and better than ever before. That's where KLA comes in. Whether you're early in your career or an experienced professional, you'll solve complex challenges, work alongside brilliant minds and help shape the future of technology. Group/Division The Semiconductor Products and Customers (Semi PC) business unit designs, builds and sells KLA’s product portfolio to help chip manufacturers meet high-quality standards for technologies like AI, data centers, automotive and electronic devices. Our inspection, metrology, and analytics systems detect and monitor defects across logic and memory chips, specialty semiconductors, wafers, reticles, advanced packaging, process equipment and materials. We also develop technologies for specialty processes, IC substrates, and PCB manufacturing, including etch and deposition, component inspection, and imaging and analytics. The e-Beam division plays a critical role in KLA’s inspection ecosystem, with tools that deliver high-resolution, timely insights into defects and critical locations on the wafer. Used alongside KLA’s patterned and bare wafer inspectors, e-Beam systems help our customers quickly identify and understand yield-impacting issues—enabling faster, more effective actions to correct the manufacturing process. What You'll Do In this role, you will play a key part in advancing business priorities by delivering high-impact work across your area of expertise. Calibration is fundamental to the accuracy, stability and productivity of semiconductor inspection and metrology systems. KLA is seeking a Senior Applied Scientist to rethink how electron-optical systems are calibrated and recalibrated—using machine learning, mathematical and statistical modeling, physics-informed AI and large-scale engineering data. You will develop next-generation calibration solutions that reduce calibration time, improve accuracy and repeatability, compensate for drift, strengthen tool-to-tool matching, and enable more intelligent diagnostics and recovery. This role combines advanced modeling with real experimental systems: you will develop algorithms, validate them against hardware data and work with engineering teams to integrate successful solutions into products. The underlying mission and scope come directly from the team’s need to modernize calibration, diagnostics and optimization across electron sources, electron-optical columns, steering systems, detectors, alignment, focus, high-voltage, and vacuum-related performance. What You Will Do Redesign calibration and recalibration workflows to improve speed, accuracy, repeatability, robustness, and system productivity. Develop and validate calibration algorithms for electron sources, electron-optical columns, deflectors and beam-steering systems, detectors, alignment and focus functions, and performance compensation associated with high-voltage and vacuum behavior. Apply machine learning, statistical modeling and physics-informed AI to predict calibration outcomes, model system behavior, and improve calibration decisions. Build methods to detect drift, anomalies and performance excursions using engineering, operational and fleet-level data. Optimize calibration sequences to reduce time, eliminate unnecessary steps, and balance accuracy, throughput, and system risk. Develop adaptive and self-correcting approaches that respond to changing system conditions and support more autonomous calibration behavior. Create intelligent diagnostic and recovery methods that accelerate root-cause identification and restore system performance after calibration or hardware excursions. Build and combine physics-based and data-driven models, including correlation models, response models, sensitivity studies, uncertainty estimates and error-propagation analyses. Analyze large, heterogeneous engineering datasets to identify performance patterns, tool-to-tool differences, failure modes, and opportunities for improvement. Design and execute experiments to evaluate calibration repeatability, reproducibility, robustness, and generalization across operating conditions. Correlate model predictions with experimental measurements and use discrepancies to refine models, algorithms, and calibration strategies. Partner with electron optics, systems, software, algorithm, manufacturing, and service teams to translate system requirements into deployable calibration and monitoring solutions. Support the full path from concept through algorithm implementation, experimental validation, product qualification, and production deployment. Continuously evaluate advances in scientific machine learning, optimization, anomaly detection, surrogate modeling, uncertainty quantification, and intelligent control. Required Qualifications M.S. or Ph.D. in Physics, Applied Physics, Applied Mathematics, Statistics, Data Science, Electrical Engineering, Computer Science, Optical Engineering, or a related quantitative discipline. Three or more years of relevant industrial or applied research experience. Relevant doctoral or postdoctoral work may qualify when it demonstrates substantial ownership of applied modeling, algorithm development, experimentation or engineering-data analysis. Demonstrated experience developing and validating machine-learning, statistical or mathematical models for complex physical, scientific or engineering problems. Strong foundation in several of the following: Numerical optimization Probability and statistics Linear algebra and numerical methods Regression, estimation, or system identification Time-series or multivariate analysis Sensitivity analysis, uncertainty quantification, or error propagation Proficiency in Python and practical experience with a modern ML or scientific-computing framework such as PyTorch, TensorFlow, JAX, scikit-learn, SciPy or a comparable environment. Experience working with substantial engineering, experimental, simulation, sensor, telemetry, or operational datasets. Evidence of personally developing or materially improving an algorithm, model, or optimization method—not solely applying an off-the-shelf model. Experience designing experiments, evaluating model performance, and correlating analytical or model predictions with measured results. Ability to independently frame ambiguous technical problems, test hypotheses, evaluate tradeoffs and communicate conclusions to cross-functional engineering partners. Preferred Qualifications Experience with calibration, system identification, controls, diagnostics, drift compensation, predictive maintenance or condition monitoring. Familiarity with electron-optical systems, SEM, charged-particle optics, semiconductor inspection or metrology equipment. Experience with advanced scientific instruments, imaging systems, robotics, autonomous systems, aerospace systems, medical imaging, industrial equipment or other sensor-rich physical platforms. Experience with fleet data, machine telemetry, high-dimensional time-series data or distributed engineering-data environments. Familiarity with physics-informed neural networks, neural operators, surrogate models, differentiable simulation, inverse problems or hybrid physics/data models. Experience with design of experiments, statistical process control, robust optimization, Bayesian methods or uncertainty-aware modeling. Experience accelerating scientific workloads with GPUs, HPC or parallel computing. Experience translating Python or research prototypes into production software; C++ experience is helpful but not required. Experience contributing algorithms to a released product or operational engineering workflow. Minimum Qualifications Doctorate (Academic) Degree and 0 years related work experience; Master's Level Degree and related work experience of 3 years; Bachelor's Level Degree and related work experience of 5 years About KLA We provide advanced inspe