Routing Optimization Engineer

About the Role

As a Routing Optimization Engineer at Naveera, you'll design and build the mathematical and algorithmic engine that powers large-scale vehicle routing, dispatching, and network optimization across our platform. You will work at the intersection of graph theory, combinatorial optimization, and high-performance systems engineering to solve complex real-world transportation problems such as shortest paths, time-dependent routing, vehicle routing with operational constraints, and large-scale fleet optimization. This role is ideal for someone with a strong mathematical, algorithmic, and implementation background who can translate advanced optimization methods into production-grade routing systems.

Responsibilities

• Design, implement, and improve core routing and dispatch optimization algorithms for real-world transportation and logistics problems
• Build and maintain solvers for shortest path, k-shortest paths, time-dependent routing, network flows, minimum spanning trees, graph cuts, connectivity analysis, and related graph problems
• Develop and optimize exact and heuristic methods for TSP, VRP, CVRP, VRPTW, pickup and delivery, multi-depot routing, heterogeneous fleet routing, and other constrained fleet optimization 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 optimization problems
• Design practical heuristics and metaheuristics including greedy algorithms, local search, large neighborhood search, tabu search, simulated annealing, genetic algorithms, and ant colony optimization
• Implement dynamic programming, approximation algorithms, stochastic optimization, robust optimization, and multi-objective optimization techniques where appropriate
• Work with optimization solvers such as Gurobi, CPLEX, and OR-Tools, while also building custom algorithms from scratch when solver-based methods are insufficient
• Model uncertainty in travel times, demand, and operational conditions using probabilistic methods and basic Markov decision process concepts where relevant
• Design efficient graph and routing data structures for large-scale computation, including priority queues, heaps, Fibonacci heaps, union-find, segment trees, and sparse/dense graph representations
• Write high-performance production code in C++ or Rust and support high-performance Python implementations for experimentation and integration
• Profile, benchmark, and optimize algorithm performance with close attention to runtime, memory usage, numerical stability, and scalability
• Contribute to parallel and concurrent algorithm design for large-scale graph processing and optimization workloads
• Collaborate closely with product, operations, and engineering teams to translate transportation constraints into mathematically sound and operationally practical solutions

Required Qualifications

• 3+ years of experience in optimization engineering, algorithm engineering, operations research, applied mathematics, computer science, or a related field
• Strong foundation in graph algorithms, shortest path methods, network flows, spanning trees, connectivity, cuts, strongly connected components, and weighted/directed graph modeling
• Strong knowledge of combinatorial optimization, especially TSP, VRP, and major routing variants
• Experience formulating and solving problems using LP, IP, MIP, CP, and related optimization methods
• Solid understanding of duality, decomposition methods, branch-and-bound, cutting planes, and modern exact optimization workflows
• Experience with heuristic and metaheuristic design for large-scale optimization problems
• Strong programming ability in C++ or Rust for performance-critical systems
• Strong Python skills for prototyping, experimentation, benchmarking, and solver integration
• Experience using optimization solvers such as Gurobi, CPLEX, or OR-Tools
• Ability to implement algorithms from scratch and not rely solely on off-the-shelf libraries
• Understanding of complexity analysis, NP-completeness, reductions, and tradeoffs between exact and approximate methods
• Experience with profiling, benchmarking, and memory/runtime optimization in production systems

Preferred Qualifications

• Advanced degree in Operations Research, Applied Mathematics, Computer Science, Industrial Engineering, or a related quantitative field
• Experience building production routing engines for logistics, transportation, rideshare, delivery, or fleet management platforms
• Familiarity with time-dependent routing, map/network preprocessing, contraction-based methods, and large-scale road graph optimization
• Experience with stochastic or robust optimization under uncertain travel times, trip demand, or service conditions
• Experience with multi-objective optimization balancing cost, service quality, fairness, utilization, and operational risk
• Familiarity with pickup and delivery systems, NEMT operations, healthcare transportation constraints, or compliance-aware scheduling
• Experience designing parallel, concurrent, or distributed optimization systems
• Strong intuition for turning mathematically elegant approaches into practical, production-ready routing systems under real operational constraints