About the Role

We are looking for an AI Engineer with strong expertise in Scientific Machine Learning (SciML), Neural Operators, PDE Learning, and Physics-Informed AI. The ideal candidate will have hands-on experience building machine learning models for scientific and engineering problems involving simulations, surrogate modeling, and computational physics.

Key Responsibilities

- Develop and deploy Scientific Machine Learning (SciML) solutions for complex engineering and simulation problems.
- Design and implement Neural Operator architectures such as Fourier Neural Operators (FNO) and Physics-Informed Neural Operators (PINO).
- Build surrogate models for accelerating physics-based simulations and numerical solvers.
- Work on PDE (Partial Differential Equation) learning and operator-learning frameworks.
- Apply Physics-Informed AI approaches to real-world scientific and industrial use cases.
- Collaborate with research and engineering teams to optimize model performance and scalability.
- Analyze model convergence, training behavior, and generalization across varying conditions.
- Leverage GPU-accelerated computing environments for large-scale model development.

Required Skills
- Robust programming skills in Python.
- Hands-on experience with PyTorch, TensorFlow, or JAX.




- Experience with Scientific Machine Learning (SciML).
- Knowledge of Neural Operators and operator-learning concepts.
- Hands-on expertise in at least one of:

- Fourier Neural Operators (FNO)
- Physics-Informed Neural Operators (PINO)
- Physics-Informed Neural Networks (PINNs)

- MeshGraphNet

- Understanding of

- Partial Differential Equations (PDEs)

- Numerical Methods

- Surrogate Modeling

- Scientific Computing

- Computational Physics
- Experience working with GPU/HPC environments.

Preferred Skills
- Experience with DeepXDE.
- Experience with NVIDIA Modulus.
- Exposure to Computational Fluid Dynamics (CFD), Digital Twins, or simulation platforms.
- Research experience in Physics-Informed AI, Neural Operators, or PDE-based learning systems.

What We're Looking For Candidates should be able to demonstrate:
- Clear understanding of Neural Networks vs Neural Operators.
- Knowledge of inputs and outputs of Neural Operators.
- Practical understanding of when to use FNO vs PINO.
- Strong grasp of PINO architecture and training methodology.
- Ability to troubleshoot model training challenges, including convergence and loss optimization.
- Experience applying AI to scientific, simulation, or physics-based domains.

📌 AI Engineer Scientific Machine Learning (Bengaluru)
🏢 StatusNeo Technology Consulting
📍 Bengaluru