AI Research Scientist, Scientific ML
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.
Requirements
Function: Product Management
Experience Level: Mid-Senior Level