Chief Architect - Multidiscplinary Optimisation Tools

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.

This role owns the quantitative design space exploration that transforms a high-level architectural choice (airframe configuration, propulsion topology, key sizing parameters) into a Pareto-optimal design front. Considering the impact of MDO during the design phases, it is a role for someone who understands why an optimiser produces the result it does, can identify when the model is wrong, and can translate the Pareto front output into concrete design recommendations

• Develop and manage the MDO environment to automate trade-studies between conflicting engineering disciplines.
  

• Lead the high-level configuration of the aircraft, ensuring it meets mission requirements and regulatory constraints.
  

• Conduct low-fidelity simulations to predict range, payload, noise, and energy consumption.
  

• Translate market needs into hard technical specifications for the subsystem teams.
  

• Select and integrate the conceptual design toolchain: aircraft analysis
  

• Build surrogate models for computationally expensive analyses
  

• Identify the Pareto front focusing on identifying the different string of aircraft parameters and configurations 
  

• 10+ years in Aircraft Design or Systems Engineering or MDO systems; experience in eVTOL or unconventional aircraft is a massive plus.
  

• Deep knowledge of MDO frameworks and aircraft sizing software.
  

• Strong Python skills for the MDO framework
  

• Understanding of hybrid-electric propulsion integration at the aircraft level
  

• Experience with multi-objective optimisation using Pareto front methods like NSGA-II, MOEA/D, SHERPA, Adjoint-based optimization
  

Preferred Qualifications 

• Unconventional aircraft configuration experience
  

• Familiarity with surrogate modelling using system identification, Gaussian processes, neural networks
  

• Familiarity with OpenMDAO or similar MDO framework infrastructure
  

• Understanding of mathematics, particularly linear algebra, optimisation, Machine Learning, probability theory