AI Research Scientist, Scientific ML
Core
Design and deploy physics-constrained machine learning methodologies (PINNs, Bayesian design, causal inference) to drive real laboratory experiments and product development for storage systems.
Role type
Senior IC scientific ML researcher (physics-constrained AI)
Builds
Validated PINNs prototypes, active learning pipelines, causal inference frameworks, and physics-constrained synthetic data generation methods.
Domain
Scientific machine learning applied to storage systems and physical ground truth validation.
Deliverable
production ML models
Required skills
PINNs methodology design, physics-constrained loss function architecture, Bayesian deep learning, active learning acquisition function design, structural causal models (SCM), causal discovery, PyTorch or JAX expert implementation, prototype-to-documentation handoff
Preferred skills
Diffusion models for synthetic data, Graph neural networks, Neural ODEs, foundation model fine-tuning, RL for scientific discovery
Technologies
PyTorch, JAX, Diffusion models, VAEs, GNNs, Neural ODEs
Responsibilities
Design physics-constrained loss function architectures; validate digital twin ML components against domain physics; lead research into Bayesian experimental design and uncertainty quantification; develop causal inference frameworks for reliability root cause analysis; design physics-constrained generative models for synthetic data; produce validated research prototypes with complete technical documentation.
Seniority
Senior, hands-on IC