Jobs at Western Digital

Leads APJ import/export trade compliance operations, serving as a regional subject matter expert and liaison with business units, brokers, carriers, and regulators. Manages broker programs, internal controls, audits, investigations, training, regulatory updates, market-access challenges, and compliance initiatives. Supports legal entity setup, process improvements, recordkeeping, and regional compliance strategy while influencing cross-functional stakeholders and driving complex projects to completion.

Develop global commodity strategies and sourcing roadmaps, lead supplier selection and contract negotiations, manage supplier performance, and deliver savings and risk mitigation. The role partners with Engineering, Operations, and R&D on innovation and design-to-cost initiatives while producing market intelligence for critical materials. It requires strong negotiation, category management, analytics, financial, supplier relationship, and cross-functional leadership skills across global procurement operations.

Develops global commodity and sourcing strategies, leads supplier selection and negotiations, manages supplier performance and risk, and delivers cost savings. Partners with Engineering, Operations, and R&D on supplier innovation and design-to-cost initiatives. The role requires strategic sourcing, category management, contracting, cost modeling, supplier relationship management, analytics, and cross-functional leadership.

Own the architecture, deployment, and technical direction of physics-informed machine learning systems for precision product development. Responsibilities include deep learning for inspection and defect detection, anomaly detection, surrogate modeling, active learning, Bayesian experimental design, and full MLOps platform ownership. The role leads design and code reviews, mentors engineers, integrates laboratory and product-development data, and deploys production ML systems using PyTorch, MLflow, Docker, AWS EKS, Kubernetes, and observability tools.

Provides white-glove, highly responsive IT support to C-Suite executives and senior leaders. Responsibilities include troubleshooting Windows and macOS devices, administering Microsoft 365 and device-management platforms, supporting collaboration and conferencing technologies, delivering on-site event and AV support, assisting with data center hardware, documenting executive environments, protecting confidential information, and coordinating escalations. The role requires independent work, executive relationship management, discretion, after-hours availability, and domestic and international travel.

Build and maintain ML-ready pipelines for experimental engineering, sensor, and operational data. Responsibilities include feature engineering, data quality monitoring, dataset versioning and lineage, drift detection, data contracts, governance, sensor streaming ingestion, synthetic data infrastructure, and MLOps data-layer components such as feature stores and training dataset registries. The role requires strong Python, SQL, pipeline design, and foundational understanding of ML data lifecycles.

Own end-to-end machine learning systems for product development, including deep learning inspection models, real-time anomaly detection, surrogate modeling, active learning, and data-to-model interfaces. Maintain MLOps pipelines, model versions, monitoring, and CI/CD integrations. Evaluate model performance through ablation studies and uncertainty analysis, document production handoffs, review code, and mentor junior teammates.

Contribute to an AI team developing machine learning solutions for precision product development. Responsibilities include tracking experiments with MLflow, evaluating models, validating training data, building CNN-based image classification and defect detection models, packaging models with Docker, supporting CI/CD, and validating inference deployments. The role requires foundational Python and PyTorch skills, ML evaluation experience, and understanding of CNNs, surrogate modeling, and active learning.

Originate scientific machine learning methodology focused on PINNs, physics-constrained neural networks, digital twins, uncertainty quantification, Bayesian experimentation, causal ML, and synthetic data. Develop validated research prototypes, acquisition functions, and physics-constrained generative models for product development. Collaborate with storage domain experts, document methodologies for engineering handoff, support design reviews, and contribute to publications or patent disclosures.