Algorithmic Optimisation Engineer

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 are looking for someone who can build the systems for implementing the operational cadence for Urban Air Mobility in the future which depends on solving problems relating to routing, scheduling substantially different from road transports especially when aircrafts have hard energy constraints, and charging. We are looking for someone who can help build the algorithms that can optimize this while also building the simulation tools to simulate hundreds of aircraft across a city network. You will implement the physics and operational algorithms at the analytical core of our simulation platform. This ranges from aircraft energy modelling through to fleet dispatch optimisation and demand generation.

Roles and Responsibilities

• Build optimisation algorithms for operational problems across flight planning, network planning, charging strategy, dispatch under real-world constraints
  

• Design and implement simulation logic for agent-based fleet and network modelling, validated progressively from small-scale prototype to production scale
  

• Build demand modelling capabilities for markets with and without prior operational data
  

• Implement sensitivity analysis and parametric trade-off tools that allow engineers and commercial teams to quantify the impact of design and assumption changes
  

Requirements

Required Qualifications

• Experience in writing optimized C++ code
  

• Understanding of Convex Optimization/Nonlinear Optimization
  

• Experience with solving optimization problems in any domain
  

• Experience with continuous-time optimization algorithms or optimization libraries like NLopt, Ceres, IPOPT
  

• Strong fundamentals in Linear Algebra, Probability, Differential Geometry
  

• Experience implementing simulation models that must be simultaneously fast, accurate, and auditable
  

Preferred Qualifications

• Experience with software tools like Gurobi, MOSEK
  

• Agent-based modelling or discrete-event simulation experience
  

• Understanding of Markov Decision Processes and methods to solve POMDPs