Run characterization / evaluations samples on Sputter machine following the experiment plan prepared by Engineer
Line setup, KPIV buyoff & validation following machine start-up procedures, feedback any abnormality found to the Engineer and take appropriate corrective action.
Basic proficiency in interpreting and analyzing control charts and process trends....
Serve as the primary Talent Development and Inclusion partner for APAC, representing the function across all regional locations and serving as a trusted advisor to senior business and HR leaders.
Build strong, credible relationships with HR team members and business leaders to surface development needs, align on priorities, and co-design solutions that reflect both global direction and local context, while managing trade-offs between global frameworks and country needs
Understand global business outcomes and the workforce strategy and partner with functional learning teams, like Tech Talent Academy to upskill employees across APAC....
Deep Learning Systems Architecture & Ownership : Design and own CNN, U-Net, and ViT systems for precision inspection, measurement, and defect classification. Own real-time anomaly detection from sensor and time-series product development data streams. Highest day-to-day output responsibility.
Physics-Informed AI & Surrogate Modeling: Validate and deploy PINNs methodology originated by the ARS — own product development implementation, physics-constraint validation against engineering requirements, and surrogate model pipelines. You are the product development -side authority; the ARS designs the methodology; you validate and deploy it to real product development systems.
Active Learning Pipeline Architecture: Architect active learning pipelines and Bayesian experimental design frameworks — integrating ARS-designed acquisition functions with DE-supplied feature data and laboratory scheduling systems. This workstream directly reduces physical experiment cost....
Deep Learning Model Implementation & Product Development Ownership: Build, train, evaluate, and maintain CNN/U-Net/ViT models for automated inspection and measurement. Own model performance end-to-end — ablation studies, confidence calibration, product development performance monitoring.
Anomaly Detection Systems: Build and maintain real-time anomaly detection for product development sensor and time-series data streams — statistical baseline, threshold calibration, drift alerting. Sole implementation owner for this workstream.
Surrogate Modeling & Active Learning Operations: Own implementation and iteration of surrogate model pipelines and active learning systems under Technical lead’s architectural direction. Configure acquisition functions; integrate with versioned feature sets....
Feature Engineering Pipelines: Build and maintain reliable, versioned feature engineering pipelines that transform raw engineering, sensor, and operational data into structured ML-ready feature sets — delivered to the specification defined
Data Quality Frameworks: Design and operate data quality checks covering completeness, schema consistency, statistical distribution stability, and label accuracy across all AI training datasets. Alert the ML team when data quality degrades before it impacts model training. Collaborate with team who performs final downstream validation.
Data Versioning, Lineage & Drift Detection: Build and maintain training data versioning and lineage tracking — ensuring full reproducibility of all model training runs and early alerting when deployment data diverges from training distributions....
Area A — Scientific ML & PINNs Methodology Origination: Originate and advance PINNs methodology — design physics-constrained loss function architectures, validate digital twin ML components against domain physics (with storage domain expert), and deliver validated prototypes with complete technical documentation to implement. As the sole PINNs methodology originator on the team — this capability cannot be delegated or substituted.
Area B — Uncertainty Quantification & Bayesian Experimental Design (hold one of B or C): Lead research into Bayesian deep learning, active learning acquisition function design, ensemble uncertainty methods, and Bayesian experimental design frameworks for autonomous experiment selection. Transfer validated acquisition function designs for active learning pipeline integration.
Area C — Causal ML & Reliability Modeling (substitute for B if reliability-focused): Own causal inference framework product development for reliability root cause analysis — structural causal model (SCM) design, causal discovery, and causal intervention planning for product development improvement....
