Company Description
WD is building the infrastructure behind the AI-driven data economy.
As AI scales, so does data. Every interaction, every model, every system generates data that must be stored, managed, and made accessible over time. That’s where we come in.
We combine deep engineering expertise with global-scale manufacturing to deliver the storage systems that make AI possible, powering hyperscale data centers, cloud platforms, and enterprise infrastructure worldwide.
This isn’t theoretical work. It’s real systems, at real scale, people solving some of the hardest challenges in technology today.
We’re looking for people who want to build, solve, and operate at that level.
Join us and let’s shape the future of data.
Job Description
About This Role — The Mission
Most ML engineering roles at large companies mean contributing to a platform team where your work disappears into a pipeline that fifty other engineers also touch. This role is different. You will be the primary owner of the ML systems that detect product development defects, model material behavior with limited data, and select the highest-value experiments from an active learning pipeline. Your models will run in product development. Your decisions will matter immediately.
Key Responsibilities
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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.
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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.
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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.
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Data-to-Model Interface Ownership: Own the data contract between the Data Engineer and the ML model stack. Define feature specifications, validate datasets against model input requirements, and escalate data quality issues before they reach the training pipeline.
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MLOps Maintenance & Product Development Reliability: Maintain model versions, training pipelines, and containers under platform architecture. MLflow tracking, CI/CD contribution, product development monitoring, and degradation escalation.
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Junior Mentorship & Documentation: Provide code review guidance to team; document model design decisions and evaluation outcomes to production-handoff standard.
Qualifications
Requirements
Education:
- Bachelor's or Master's degree in AI, Machine Learning, Computer Science, or related field. AI major or strong AI research focus preferred.
Experience:
- 1–3 years of hands-on ML engineering experience, or equivalent depth demonstrated through internships, academic research, or open-source contributions. Must show component-level technical ownership within an end-to-end ML pipeline (training through deployment) — not just execution under direction. Kaggle rankings, arXiv preprints, or significant open-source ML contributions are valued as evidence of depth.
Must have Skills:
- Python: Strong proficiency — primary ML development language
- PyTorch: Proficient → Expert — independent model training and evaluation
- Computer Vision: Strong foundation in CNNs, plus hands-on depth in at least one of: U-Net/segmentation, ViT/transformer-based vision, or time-series anomaly detection. Candidates with depth across multiple areas (e.g. full inspection-scope coverage — segmentation, transformer vision, and anomaly detection together) will be considered for the higher end of the band.
- Surrogate Modeling: Implement and iterate surrogate pipelines under P110 architectural guidance
- Active Learning: Configure acquisition functions; uncertainty-guided experiment scheduling
- Data-to-Model Interface: Define feature specs; validate incoming datasets against model requirements; flag data quality issues before training
- Practical MLOps: MLflow, Docker, Git, basic CI/CD contribution
- Model Evaluation & Uncertainty Analysis: Ablation studies, confidence calibration, validation methodology
- Technical Documentation: Model design decisions and evaluation results to production-handoff standard
Good to have Skills:
- PINNs implementation under technical guidance
- Bayesian methods — Bayesian neural networks, Gaussian processes, ensemble uncertainty, calibration
- Reinforcement learning basics — gym environments, policy gradient concepts
- AWS fundamentals — S3, EC2, SageMaker basics; entry-level cloud ML deployment
- RAG pipeline fundamentals
Additional Information
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WD thrives on the power and potential of diversity. As a global company, we believe the most effective way to embrace the diversity of our customers and communities is to mirror it from within. We believe the fusion of various perspectives results in the best outcomes for our employees, our company, our customers, and the world around us. We are committed to an inclusive environment where every individual can thrive through a sense of belonging, respect and contribution.
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