Scientific Machine Learning Engineer - Join Our AI Revolution

The Challenge

We are seeking an exceptional Scientific Machine Learning Engineer who stands at the intersection of physics, mathematics, and artificial intelligence. This role demands mastery of both classical computational methods and cutting-edge machine learning techniques to solve complex physical systems that traditional approaches cannot handle.

CRITICAL REQUIREMENT: This position is reserved for candidates who are demonstrably in the top 1% of their cohort across ALL specified technical domains. We are building most advanced scientific computing capabilities and require nothing less than world-class expertise in every aspect of the role.

You will be architecting the next generation of physics-informed AI systems that will power strategic infrastructure applications. Your work will directly contribute to solving grand challenges in fluid dynamics, structural mechanics, electromagnetic systems, quantum systems, and multi-physics phenomena.

Location: Bangalore, India (On-site required for high-security projects)
Reporting: Chief Engineer

Core Responsibilities

Physics-Informed Neural Networks

Design and implement advanced PINN architectures for complex PDE systems
Develop novel loss functions incorporating physical constraints and conservation laws
Optimize PINN training for multi-scale and multi-physics problems
Create hybrid methods combining PINNs with traditional numerical methods
Implement domain decomposition techniques for large-scale PINN applications

Fourier Neural Operators

Implement and optimize FNO architectures for operator learning
Develop resolution-invariant neural operators for multi-scale problems
Create custom FNO variants for specific physical applications
Integrate FNOs with traditional computational fluid dynamics solvers
Design efficient training pipelines for large-scale operator datasets

Finite Element & Volume Methods

Implement high-performance FEM solvers for thermal and electromagnetic analysis
Develop advanced FVM schemes for computational fluid dynamics applications
Create adaptive mesh refinement algorithms for complex geometries
Optimize numerical stability and convergence for ill-conditioned problems
Integrate ML-based error estimation and mesh optimization techniques

High-Performance Computing

Design massively parallel algorithms using MPI and OpenMP
Implement GPU-accelerated solvers using CUDA and cuDNN
Optimize memory access patterns and data layout for maximum performance
Develop distributed computing solutions for multi-node clusters
Create load balancing strategies for heterogeneous computing environments

Algebraic Systems & Optimization

Implement advanced iterative solvers for large sparse linear systems
Develop preconditioners for ill-conditioned matrices from discretized PDEs
Create multigrid and domain decomposition methods for parallel solving
Optimize non-linear algebraic system solutions using Newton-Krylov methods
Design eigenvalue solvers for stability analysis and modal computations

Strategic Applications

Apply SciML techniques to aerospace computational challenges
Develop real-time simulation capabilities for autonomous systems
Create uncertainty quantification frameworks for safety-critical applications
Design inverse problem solvers for parameter estimation and design optimization
Implement multi-fidelity modeling approaches for computational efficiency

Mandatory Technical Requirements

Mathematical Foundation

Advanced PDEs: Deep expertise in elliptic, parabolic, and hyperbolic equations
Numerical Analysis: Finite difference, finite element, and finite volume methods
Linear Algebra: Sparse matrix algorithms, iterative solvers, preconditioning
Optimization Theory: Constrained/unconstrained optimization, variational methods

Programming Excellence

Python: NumPy, SciPy, PyTorch, JAX, scikit-learn, advanced optimization
C/C++: High-performance computing, memory management, template programming
CUDA: GPU programming, kernel optimization, memory coalescing, cuBLAS/cuSPARSE
Parallel Computing: MPI, OpenMP, distributed algorithms, load balancing

Machine Learning Expertise

Physics-Informed Neural Networks: Implementation, training, and optimization
Fourier Neural Operators: Operator learning, spectral methods integration
Neural ODEs/PDEs: Continuous-time models, adjoint methods
Scientific ML: Symbolic regression, equation discovery, multi-fidelity modeling

Essential Experience

PhD in Applied Mathematics, Computational Physics, or Mechanical Engineering
5+ years of hands-on experience in computational science applications
Published research in top-tier journals (JCP, SIAM, Physics of Fluids, etc.)
Track record of implementing production-grade scientific computing software

What We Offer

World-Class Resources

Access to cutting-edge HPC clusters, latest GPUs and state-of-the-art development environments for pushing the boundaries of scientific computing.

Elite Compensation

Industry-leading salary package competitive with top global tech companies, performance bonuses, equity participation, and comprehensive benefits.

Research Excellence

Opportunity to publish groundbreaking research, attend international conferences, collaborate with leading scientists, and contribute to open-source scientific software.

National Impact

Direct contribution to India's strategic technological capabilities while working on applications that will define the future of computational science and engineering.

Performance Expectations

10^9+

DOF Systems

1000+

CPU/GPU Hours/Week

100x

Speedup Targets

99.9%

Numerical Accuracy

Candidate Profile

You are among the world's leading experts in scientific machine learning, with deep expertise spanning classical computational methods and modern AI techniques. Your work has advanced the state-of-the-art in physics-informed AI, and you have a proven track record of implementing production-grade systems that solve real-world problems.

Non-Negotiable Standards: You must demonstrate exceptional mastery in ALL technical areas listed above. This role requires someone who can seamlessly transition between mathematical theory, algorithmic development, and high-performance implementation while maintaining the highest standards of scientific rigor.

You thrive on solving previously unsolvable problems and understand that your work will directly impact India's technological sovereignty. You are passionate about advancing both scientific understanding and practical applications that serve national strategic interests.

Ready to Define the Future of Scientific Computing?

Join India's most ambitious scientific AI initiative and architect the computational foundation of our nation's future.

Selection Process:

Technical assessment (90 min) → Coding challenge (take-home) → Technical presentation (120 min) → Deep-dive interview (90 min) → Final evaluation with leadership

Note: Only candidates demonstrating exceptional competency in ALL required areas will advance to the final rounds.