Software Engineering Intern, Glydways
Dispatch, Fleet Optimization
Autonomous electric transportation
Be an early applicant
Autonomous electric transportation
101-200 employees
Be an early applicant
101-200 employees
Company mission
To provide a 24/7 mobility service solution that is environmentally net-negative in terms of greenhouse gas production (GHG), and, inherently profitable.
Role
Who you are
- 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
What the job involves
- The Dispatch team is responsible for how the Glydways fleet moves as a coordinated system. We design and implement algorithms for:
- Fleet-level planning and optimization (e.g., load-balancing vehicles across stations, choosing when and where to charge)
- Real-time routing and deconfliction to ensure vehicles move safely and efficiently through various zones of a transit system
- Large-scale simulations used to size fleets, evaluate new system designs, and validate new algorithms before they reach real vehicles
- The team is a mix of software engineers and algorithm specialists with backgrounds in optimization, robotics, applied math, and large-scale simulation
- We work primarily in C++ and Python, with a strong emphasis on high-quality code, rigorous testing, and reproducible experiments
- As an intern, you will collaborate closely with Dispatch engineers and cross-functional partners (e.g., Motion, Simulation, Data, and Commercial) on problems that directly affect system performance and commercial projects
- 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 (e.g., “How many vehicles do we need for this project?” or “What if charging is slower?”) 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
Our take
Although more than 4000 cities across the globe meet the threshold for necessary urban mass transit systems, under 5% of them have the capacity to make it a reality. Glydways' autonomous electricity-powered transport systems could provide a cost-effective and environmentally friendly alternative for the 95% without rail or transport systems.
Glydways' Personal Rapid Transit systems consist of battery-operated electric vehicles called Glydcars that travel on dedicated rights of way. These “roads” can be built above ground level or through tunnels, and can integrate with other forms of public transport already existing within the city or town.
Whilst the addition of Glydcars' infrastructure raises questions about additional costs, the company’s mass transit solution has nonetheless attracted positive attention and serious investment from the likes of Bill Gates' venture fund, Gates Frontier. Its Series C round of funding was followed by news that Glydway is involved in over 100 different mass transit projects, with a world tour of its vehicles taking place through 2025.
Kirsty
Company Specialist at Welcome to the Jungle
Company
Funding (2 rounds)
May 2024
$20m
SERIES B
Oct 2023
$56m
SERIES B
Total funding: $76m
Company HQ
Downtown, South San Francisco, CA
Leadership
Mark Seeger
(Co-Founder & Co-CEO)Previously Director of Digital Innovation at Ogilvy. Founded M Jeans and GreenIdea, and was a Product Design Engineer at Apple.
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