Fleet Planning Engineer

Meet the team:

You'll be joining a mission-driven autonomy engineering team focused on coordination, orchestration of resources, and task scheduling for autonomous vehicles. Our work spans from centralized fleet planning to onboard vehicle decision-making, blending algorithmic rigor with practical system implementation. We are passionate about building reliable, real-world autonomy solutions that can scale. The team values thoughtful design, clear documentation, and strong collaboration across disciplines including product, infrastructure, system engineering, and testing. We take pride in balancing deep technical expertise with a pragmatic approach to getting impactful solutions into production.

Required Skills:

• Strong grounding in mixed-integer linear programming, metahuristic methods, machine learning techniques.
• Familiarity with dynamic resource allocation algorithms.
• Understanding of queueing systems and flow equilibrium models (for balancing supply vs demand).
• Ability to design or interpret simulation experiments to evaluate routing or balancing strategies.
• Comfortable deriving or reasoning with continuous relaxations of discrete problems.
• Experience with stochastic or probabilistic modeling (Poisson processes, demand forecasting).
• Knowledge of graph algorithms (shortest path, matching, network flow).
• Strong programming proficiency in C++, including writing efficient, numerically stable algorithms. Experience with modern build/test tooling (e.g., Bazel).
• Systems reliability and operations mindset: observability, debugging at scale, graceful degradation, and deployment patterns.

Roles and Responsibilities:

• Design and implement algorithms that dynamically allocate vehicles to meet time-varying passenger demand.
• Formulate and solve optimization problems (e.g., MILP, QP, network flow) for vehicle routing, rebalancing, and resource allocation.
• Develop heuristics or approximations for real-time decision-making when full optimization isn’t tractable.
• Implement feedback-based strategies to maintain stable fleet utilization.
• Build mathematical and simulation models representing vehicle movement, demand patterns, and station queues. Derive and maintain performance metrics (e.g., wait time, idle miles, fairness, stability).
• Analyze fleet utilization, imbalance, and throughput to identify bottlenecks or inefficiencies.
• Conduct what-if studies using simulated origin/destination demand matrices, travel times, and network layouts to stress-test load-balancing strategies.