AI Research Scientist, Scientific ML

Western Digital Corporation; Singapore; Hybrid; SGD 120,000 - 190,000; Full time

Job summary

Western Digital Corporation is seeking a researcher to design physics‑constrained PINNs for product development, driving ML engineers’ work from conception to implementation. You will validate models against domain physics and deliver prototypes with complete documentation for team handoff.

The role emphasizes governance of scientific ML methodologies, uncertainty quantification, and data-driven reliability analysis within a storage systems context.

Qualifications

• Master's or PhD in AI/ML/Physics with strong research focus and scientific computing background.

Responsibilities

• Originate and advance PINNs methodology with physics-constrained loss architectures and digital twin modeling.
• Lead Bayesian deep learning, active learning, and ensemble uncertainty research for autonomous experiments.
• Own causal inference framework development for reliability and yield analysis when applicable.
• Design physics-constrained generative models for synthetic data and ensure handoff with documentation.

Skills

PyTorch/JAX

PINNs

Physics constraints

Digital twin

Bayesian deep learning

Active learning

Structural causal models

Research output

Education

Master's or PhD in AI/ML/Physics

Tools

PyTorch

JAX

Job description

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 peoplewho 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.
• IP & Domain InterMface: Demonstrate strong research output through preprints, or patent disclosures. Interface with storage domain expert to validate physics constraints before deployment. Produce validated research prototypes with complete technical documentation to team-handoff standard. Participate in design reviews as the research methodology authority.

Qualifications

Requirements

Education:

• Master's or PhDin Artificial Intelligence, Machine Learning, Physics, Applied Mathematics, or related field. Strong AI/ML research focus and scientific computing background required.

Experience:

• For Master's degree:1–3 years work or research experience in scientific ML or applied AI roles.For PhD:Open — no minimum work experience required. Research depth is the primary criterion. Peer-reviewed publication (NeurIPS, ICML, ICLR, AAAI, Nature MI, or domain-specific venues), strong PhD research, or significant open-source scientific ML contribution.

Must have skills:

• PyTorch or JAX:Expert — deep research-level implementation capability
• Area A — Scientific ML & PINNs (Required):PINNs methodology design, physics-constrained loss function architecture, digital twin modeling. The most critical capability on the team.
• Area B — UQ & Bayesian Methods (one of B or C required):Bayesian deep learning, Bayesian experimental design, active learning acquisition function design, ensemble uncertainty quantification
• Area C — Causal ML (substitute for B if reliability-focused):Structural causal models (SCM), causal discovery, causal inference for reliability and yield root cause analysis
• Demonstrated Research Output:Publication, preprint, PhD thesis chapter, or significant open-source scientific ML contribution in at least one primary area
• Prototype-to-Documentation Handoff:Produce validated research prototypes with complete technical documentation

Good to have skills:

• Diffusion models (DDPM, conditional diffusion) — physics-constrained synthetic data generation
• Graph neural networks (GNN) — materials property prediction, failure propagation modeling
• Neural ODEs — dynamic systems and degradation trajectory modeling
• Foundation model fine-tuning — domain adaptation for scientific tasks
• RL for scientific discovery — exploration strategies in experimental search spaces

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.

WD is committed to offering opportunities to applicants with disabilities and ensuring all candidates can successfully navigate our careers website and our hiring process. Please contact us at [e-mail] to advise us of your accommodation request. In your email, please include a description of the specific accommodation you are requesting as well as the job title and requisition number of the position for which you are applying.

Notice To Candidates: Please be aware that WD and its subsidiaries will never request payment as a condition for applying for a position or receiving an offer of employment. Should you encounter any such requests, please report it immediately to WD Ethics Helpline or email [e-mail] .

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.

WD is committed to offering opportunities to applicants with disabilities and ensuring all candidates can successfully navigate our careers website and our hiring process. Please contact us at [e-mail] to advise us of your accommodation request. In your email, please include a description of the specific accommodation you are requesting as well as the job title and requisition number of the position for which you are applying.

Notice To Candidates: Please be aware that WD and its subsidiaries will never request payment as a condition for applying for a position or receiving an offer of employment. Should you encounter any such requests, please report it immediately to WD Ethics Helpline or email [e-mail] .

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