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
Precision product development is one of the last frontiers where AI has not yet been systematically deployed at the physics level. We are changing that. As the ML Technical Lead on this team, you will be the person who makes physics-informed AI work in product development — not as a research prototype, but as a deployed system that drives real engineering decisions. If that is the kind of problem you want to work on, we want to talk to you.
Key Responsibilities
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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.
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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.
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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.
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MLOps Platform Ownership: Own the full ML platform: MLflow, Docker, AWS EKS/Kubernetes, LLM Gateway (LangFuse/PortKey), CI/CD, observability, and model monitoring. Architect the platform; delegate maintenance to team once stable.
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Technical Direction & Team Architecture: Lead design reviews and code reviews; provide technical direction to team; define data interface contracts with the DE; partner with domain scientists from problem definition through deployment; mentor junior team members.
Qualifications
Requirements
Education
- Bachelor's or Master's degree in Artificial Intelligence, Machine Learning, Computer Science, or related field. AI major or strong AI research focus preferred. Equivalent depth demonstrated through open-source contributions, or significant GitHub portfolio will be considered.
Experience
- Minimum 3-5 years of hands-on technical experience
- Demonstrated technical ownership of AI systems from design to product development deployment.
- Proven direction of a small ML or AI engineering team —clear technical leadership with evidence of design reviews, mentoring, and cross-functional delivery.
- Track record of shipping ML models into product development.
- Experience in materials science, semiconductor, precision product development, or equivalent scientific/industrial AI is a strong differentiator.
- Significant open-source ML contributions, public technical writing, or a documented GitHub portfolio demonstrating scientific ML or product development ML engineering depth will be evaluated alongside work experience records.
Must have Skills:
- Python: Expert-level proficiency - Primary language for all ML System work
- PyTorch: Expert - Custom loss functions, full training loop ownership
- Computer Vision: CNN, U-Net, ViT — full architecture range for inspection, defect detection, and measurement
- Anomaly Detection: Real-time anomaly detection from sensor and time-series product development data streams
- Surrogate Modeling: Data-efficient ML in limited-data scientific regimes; end-to-end pipeline ownership
- Active Learning & Bayesian Experimental Design: Acquisition function pipelines; laboratory scheduling and instrument control integration
- Product Development MLOps: MLflow, Docker, AWS EKS, Observability Platforms like LangFuse, PortKey or other LLM Gateways)
Good-to-Have Skills:
- PINNs: Validate ARS-originated designs; own physics-constrained product development models.
- Reinforcement Learning:
- RAG / GraphRAG — retrieval-augmented generation pipeline ownership
- LangFuse / PortKey — LLM Gateway and agent observability
- RLHF & reward modeling — domain expert feedback integration
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AWS
- LLM Finetuning (LoRA, QLoRA)
- LLM Distillation
Additional Information
#LI-FN1
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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