Staff Engineer - Machine Learning & Optimization Systems

About 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 and tooling leveraging optimisation, multi-physics models, and machine learning to be used across our engineering, operations, and commercial teams. 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. This role will lay the the architectural and algorithmic foundation for our internal engineering design optimisation and operations suite Roles and Responsibilities Design the optimisation and representation architecture of our platform across both internal engineering tools and aircraft operations software Build and validate coupled ML models and systems across complex multi-physics systems, planning and optimisation systems Develop the algorithmic tooling to evaluate trade-offs between aircraft parameters parameters (e.g., rotor configuration, battery size) and fleet-level metrics (e.g., vertiport throughput, community noise impact). Architect the C++ and Python APIs that connect the underlying physics solvers to user-facing engineering design tools and macro-level network simulators Own verification and validation, and tell the Engineering teams on where a model can and can't be trusted. Requirements Required Qualifications Expertise in advanced autonomous systems where optimization, Machine Learning, and software architecture meet Understanding of mathematics, particularly linear algebra, optimisation, Machine Learning, probability theory, differential geometry Deep understanding of mathematical optimization across gradient based optimisation to non-gradient based optimization Strong practical experience with ML frameworks (PyTorch, JAX) and applying ML to physical or simulated environments Understanding of Markov Decision Processes and methods to solve POMDPs Exceptional proficiency in modern C++ and Python. You know how to build maintainable, high-performance software systems. Prior experience in architecture design for data/ML systems; defining success metrics, delivery plans and quality Experience with 3D or other irregular data: meshes, point clouds, voxels, sparse tensors. Proven ability to implement and optimize high-throughput spatial queries using R-trees, Bounding Volume Hierarchies, KD-trees, or geospatial indexing systems Preferred Qualifications Familiarity with surrogate modelling using system identification, Gaussian processes, neural networks Expertise with Automatic differentiation tools and development Deep knowledge of MDO frameworks and aircraft sizing software. Understanding of hybrid-electric propulsion integration at the aircraft level