Software Engineering Intern – Dispatch, Fleet Optimization
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Job Description
Software Engineering Intern optimizing fleet operations at Glydways, improving transportation accessibility and sustainability through innovative algorithms and simulations.
Responsibilities:
- Prototype and evaluate fleet optimization algorithms for problems like vehicle rebalancing, charging strategies, and maintenance/cleaning scheduling.
- Explore reinforcement learning–based approaches for selected dispatch decisions.
- Design and run simulation experiments to compare algorithm variants using various metrics.
- Contribute production-quality code to the Dispatch codebase in C++ and/or Python.
- 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 and incorporate them into models and algorithms.
- Participate in code reviews and design discussions.
Requirements:
- 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, or stochastic optimization, Heuristics or metaheuristics (e.g., simulated annealing, genetic algorithms, search-based methods).
- 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, Experience training and evaluating RL policies in simulated environments 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.
Benefits:
- Equal employment opportunities to all employees and applicants
- Prohibits discrimination and harassment of any type
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