Software Engineering Intern (Dispatch – Fleet Optimization)

In this internship, you will:

• Prototype and evaluate fleet optimization algorithms for problems like vehicle rebalancing, charging strategies, and maintenance/cleaning scheduling (e.g., using mixed-integer optimization, dynamic programming, or heuristic/metaheuristic methods).
• Explore reinforcement learning–based approaches for selected dispatch decisions (e.g., when to send vehicles to charge, how to route vehicles through busy junctions), including state representation, reward design, and basic policy evaluation in simulation.
• Design and run simulation experiments to compare algorithm variants (optimization- or RL-based) using metrics such as wait time distributions, fleet utilization, energy usage, and robustness under disruptions.
• Contribute production-quality code to the Dispatch codebase in C++ and/or Python, including unit tests, integration tests, and clear documentation.
• Collaborate with teammates to translate high-level operational or commercial questions into well-posed optimization or simulation studies.
• Work with other autonomy and platform teams to understand constraints coming from motion limits, energy usage, and infrastructure design, and incorporate them into your models and algorithms.

Participate in code reviews and design discussions, giving and receiving feedback to improve both code quality and overall system design.

This role contributes to Glydways' mission of revolutionizing transit with a carbon-neutral, accessible, and affordable transportation system, helping create a future where everyone has the freedom to move and empowering communities to thrive through improved mobility.

We're looking for someone who brings:

• Academic background in computer science, operations research, robotics, electrical engineering, applied mathematics, or a related field.
• Current undergraduate (rising senior) or graduate student status (MS or PhD) with relevant coursework or research in optimization and/or reinforcement learning.
• Solid programming skills in at least one of: C++ (preferred for production code), and/or Python (preferred for prototyping, data analysis, and RL/optimization experiments).
• Coursework or experience in optimization, such as: Linear/integer/mixed-integer programming, dynamic programming, approximate dynamic programming, stochastic optimization, heuristics or metaheuristics.
• Coursework or experience in reinforcement learning, such as: Markov decision processes, value-based and/or policy-based methods, function approximation (e.g., neural networks) and experience with a framework like PyTorch or TensorFlow is a plus.
• Strong grasp of algorithms, data structures, and complexity, and comfort reasoning about performance trade-offs in large-scale systems.
• Familiarity with probability, statistics, and simulation, including designing experiments and interpreting results.
• Software engineering fundamentals: Comfort working in a Linux environment, experience with version control (git) and collaborative development workflows, writing clear, maintainable, and tested code.
• Ability to communicate technical ideas clearly, both in writing and in discussions, and to collaborate effectively with teammates from different disciplines.

No specific benefits, compensation, or salary information mentioned in the job posting.

Glydways is reimagining public transit with a mission to revolutionize transportation through a carbon-neutral, interconnected transit system using autonomous vehicles on dedicated roadways, aiming to make mobility more accessible, affordable, and sustainable to unlock economic and social prosperity.