AI Research Scientist, Scientific ML

Location: Singapore office

Company

hirify.global builds storage systems and infrastructure for AI-driven data centers, cloud platforms, and enterprise environments.

What you will do

• Originate and advance physics-informed neural network methodology, including physics-constrained loss architectures and digital twin validation.
• Research Bayesian deep learning, uncertainty quantification, active learning acquisition functions, and Bayesian experimental design for autonomous experiment selection.
• Develop causal inference frameworks for reliability root cause analysis, including structural causal models, causal discovery, and causal intervention planning.
• Design physics-constrained diffusion and VAE approaches for synthetic data generation and prepare training recipes for operational pipelines.
• Validate methodologies with storage domain experts and physical or laboratory ground truth.
• Deliver validated research prototypes, complete technical documentation, preprints or patent disclosures, and participate in design reviews.

Requirements

• Master’s or PhD in artificial intelligence, machine learning, physics, applied mathematics, or a related field, with a strong scientific computing background.
• Expert, research-level implementation capability with PyTorch or JAX.
• Demonstrated experience designing PINNs methodology, physics-constrained loss functions, and digital twin models.
• Expertise in at least one additional area: Bayesian deep learning and experimental design with uncertainty quantification, or causal ML with structural causal models and reliability analysis.
• Research output such as a publication, preprint, PhD thesis chapter, or significant open-source scientific ML contribution.
• Ability to produce validated prototypes with complete technical documentation for engineering handoff.

Nice to have

• Diffusion models, graph neural networks, neural ODEs, foundation model fine-tuning, or reinforcement learning for scientific discovery.
• Top-venue publications in scientific ML or related fields.
• Materials science, semiconductor, or precision product development experience.

Culture & Benefits

• Work on storage infrastructure supporting hyperscale data centers, cloud platforms, and enterprise systems.
• Collaborate with storage domain experts and product development teams.
• Inclusive environment focused on diversity, belonging, respect, and contribution.
• Accessibility support is available throughout the application and hiring process.