AI Research Scientist

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

This is not a generalist AI research role.

We are looking for a researcher who has spent serious time thinking about how physics constraints interact with neural network training — and who wants to see that methodology deployed against real product development problems, not just validated on benchmark datasets. You will be the person who designs what the ML engineers build. Your acquisition functions will drive real laboratory experiments. Your PINNs methodology will run in product development. The work you originate here will be tested against physical ground truth in ways that most academic scientific ML researchers never get access to.

• 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.
• Synthetic Data Methodology : Design physics-constrained generative model approaches (diffusion models, VAEs) for synthetic data generation. Deliver validated methodology and training recipes for pipeline operationalization.