About the role
from listingAbout The ePlane Company The ePlane Company is at the forefront of India's urban air mobility revolution. Incubated at IIT Madras, we are a deep-tech startup dedicated to designing and building the world's most compact electric flying taxi. Our mission is to make door-to-door flying a reality, drastically reducing commute times and decongesting our cities for a cleaner, greener future. We're a passionate team of engineers, designers, and visionaries working on cutting-edge technology, and we're looking for brilliant minds to help us take flight.
Chart the Course for the Future of Flight We're building the coupled simulation and optimisation capability that informs our aircraft design decisions for now and future. We are looking for a candidate who can build models across domains where you work with our internal ML and software development teams to lead the development of multi-physics simulations and models. You will join our team to transform mathematical models, aerospace physics simulations, and Reinforcement Learning algorithms into high-throughput, low-latency, production-grade GPU-accelerated software engines.
Roles and Responsibilities implement high-performance, differentiable numerical solvers from the ground up Build and validate coupled ML models and systems across complex multi-physics systems Own verification and validation, and tell the Engineering teams on where a model can and can't be trusted Chain surrogate models built by other teams to enable engineering teams to utilise them Ensure all physical simulations expose clean, analytic gradients (autodiff) so the Optimization team can perform rapid gradient-based design sweeps Profile and optimize simulation code for maximum throughput on GPU architectures, focusing on memory efficiency and execution speed Build surrogate and reduced-order models from our simulations.
These should report their own uncertainty, combine cheap and expensive simulations, and choose where to sample next Requirements Required Qualifications Direct experience in physics engine development, computational engineering software, or high-fidelity simulation environments Understanding of mathematics, particularly linear algebra, numerical simulations, probability theory, differential geometry Hands-on experience building numerical solvers in NVIDIA Warp, JAX, or writing custom high-performance CUDA/C++ compute kernels Deep knowledge of MDO frameworks and aircraft sizing software.
Familiarity with surrogate modelling using system identification, Gaussian processes, neural networks Expertise with Automatic differentiation tools and development Preferred Qualifications Deep knowledge of MDO frameworks and aerospace sizing software. Experience with OpenMDAO or a similar framework for coupling and optimising engineering models. Experience with multi-objective optimisation using Pareto front methods like NSGA-II, MOEA/D, SHERPA, Adjoint-based optimization Experience with 3D or other irregular data: meshes, point clouds, voxels, sparse tensors Understanding of hybrid-electric propulsion integration at the aircraft level
Experience: 4-5 years
Employment type: Full time
Industry: Technology