Routing Optimization Engineer

Role overview This position centers on designing and building the algorithmic engine behind large-scale vehicle routing, dispatching, and network optimization for a transportation platform. The work sits at the intersection of graph theory, combinatorial optimization, and high-performance systems engineering, tackling real-world problems such as shortest paths, time-dependent routing, and constrained fleet optimization. It is ideal for someone with a strong mathematical, algorithmic, and implementation background who can move advanced optimization methods into production-grade systems.

Responsibilities - Design, implement, and continuously improve core routing and dispatch optimization algorithms for transportation and logistics problems - Build and maintain solvers for shortest path, k-shortest paths, time-dependent routing, network flows, minimum spanning trees, graph cuts, and connectivity analysis - Develop exact and heuristic methods for TSP, VRP, CVRP, VRPTW, pickup-and-delivery, multi-depot, and heterogeneous fleet routing problems - Formulate routing and scheduling problems using LP, IP, MIP, CP, and related mathematical optimization techniques - Apply decomposition methods such as Lagrangian relaxation, column generation, branch-and-bound, branch-and-cut, and branch-and-price for large-scale problems - Design practical heuristics and metaheuristics including greedy algorithms, local search, large neighborhood search, tabu search, and simulated annealing

Requirements - Strong background in mathematics, algorithms, and high-performance systems engineering - Ability to translate advanced optimization methods into production-grade routing systems - Experience applying combinatorial optimization techniques to real-world transportation or logistics problems

Benefits and work setup - Remote, full-time role within the Math & ML team