AI Engineer Scientific Machine Learning

Statusneo Technology Consulting; Bengaluru; On-site; INR 1,800,000 - 3,200,000; Full time

Job summary

Statusneo Technology Consulting in Bengaluru invites an AI Engineer specializing in Scientific Machine Learning to design and deploy SciML solutions for engineering simulations. The role requires building surrogate models and leveraging PDE learning to accelerate physics-based solvers.

You will implement Fourier Neural Operators, PINO, PINNs, and MeshGraphNet, work with PyTorch/TensorFlow/JAX, and collaborate with research teams while optimizing performance on GPU/HPC environments.

Qualifications

• Strong background in SciML and neural operators.
• Proficiency in Python for scientific computing.
• Hands-on experience with GPU-accelerated ML frameworks.
• Experience building surrogate models for simulations.
• Familiarity with PDEs and numerical methods.

Responsibilities

• Develop and deploy SciML solutions for engineering/simulation problems.
• Design and implement Neural Operator architectures (FNO, PINO).
• Build surrogate models to accelerate physics-based simulations.
• Work on PDE learning and operator-learning frameworks.
• Apply Physics-Informed AI to real-world use cases.
• Collaborate with research/engineering teams to optimize performance.
• Analyze convergence, training behavior and generalization.
• Leverage GPU/HPC environments for large-scale development.

Skills

Python

Neural Operators

SciML

PDE Learning

Physics-Informed AI

FNO

PINO

PINNs

MeshGraphNet

GPU/HPC

Numerical Methods

Tools

PyTorch

TensorFlow

JAX

DeepXDE

NVIDIA Modulus

Job description

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

• Strong 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.