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Specialized physics-informed ML role and senior requirement reduce applicant density.
Physics-informed ML and simulation expertise limits transferability across industries.
Explicit 7+ years requirement and specialized ML/simulation skills make filters strict.
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Lead design and implementation of Physics-Informed Neural Networks (PINNs) and neural ODE solvers for physics-based simulations.
Own the full MLOps lifecycle for ML models powering real-time scenario evaluation and digital twin intelligence, including feature engineering, model training, deployment, and monitoring.
Build and optimize scalable ML pipelines and hybrid architectures combining data-driven learning with physics constraints using Databricks ML, MLflow, and containerized deployment.
7+ years of experience in ML engineering, applied ML, or scientific computing.
Master’s or PhD in Computer Science, Machine Learning, Computational Science, Physics, or related field.
Proven track record of deploying ML models into production at scale.
Experience with physics-based or scientific ML applications.
Experienced with production-grade MLOps workflows including experiment tracking, model versioning, serving, monitoring, and CI/CD.
Technical expertise at the intersection of ML and physics-based simulation, including neural ODEs and constraint-aware ML models.
Background or exposure to digital twin, simulation platforms, and industrial domains like Manufacturing, Logistics, or Transportation.