Application Guide

How to Apply for Fleet Planning Engineer

at Glydways

🏢 About Glydways

Glydways is pioneering net-negative GHG autonomous transportation with a unique approach: standardized autonomous vehicles on dedicated roadways that operate 24/7 without heavy infrastructure costs or taxpayer subsidies. They're not just building transit—they're creating a future where mobility is accessible, affordable, and sustainable for everyone, making this an opportunity to work on genuinely revolutionary technology with strong social impact.

About This Role

As a Fleet Planning Engineer at Glydways, you'll design and implement optimization algorithms for coordinating autonomous vehicles across their network, balancing supply and demand in real-time. This role directly impacts system efficiency, user experience, and environmental goals by ensuring optimal resource allocation and vehicle orchestration.

💡 A Day in the Life

You might start by analyzing overnight fleet performance data to identify optimization opportunities, then develop or refine MILP formulations for daily vehicle allocation. Later, you could collaborate with the autonomy team to implement MPC strategies for real-time adjustments, followed by testing algorithm changes in simulation to ensure stability before deployment.

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🎯 Who Glydways Is Looking For

  • Has hands-on experience with MILP/QP/LP solvers (Gurobi, OSQP, or CPLEX) for real-world optimization problems, not just academic exercises
  • Understands how to apply Model Predictive Control and dynamic resource allocation to autonomous vehicle fleets or similar distributed systems
  • Can translate queueing theory and flow equilibrium models into practical algorithms that balance vehicle supply with passenger demand
  • Writes production-quality, numerically stable Python or C++ code for algorithms that must run reliably at scale

📝 Tips for Applying to Glydways

1

Highlight specific projects where you used Gurobi, OSQP, or CPLEX to solve optimization problems—mention problem size, constraints, and results

2

Demonstrate understanding of Glydways' unique model (dedicated roadways, standardized vehicles, 24/7 operation) in your application materials

3

Include examples of balancing supply/demand in dynamic systems, especially if related to transportation or resource allocation

4

Show how your programming skills go beyond scripting—provide evidence of writing efficient, stable algorithms in Python or C++

5

Connect your experience to Glydways' mission by explaining how your technical work can advance affordable, sustainable mobility

✉️ What to Emphasize in Your Cover Letter

["Your experience with optimization solvers and how you've applied them to real-world problems similar to fleet coordination", "How your approach to algorithm design aligns with Glydways' need for reliable, scalable autonomy solutions", 'Specific examples of working with dynamic systems or resource allocation where timing and efficiency were critical', "Why Glydways' mission-driven approach to transportation resonates with you personally and professionally"]

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🔍 Research Before Applying

To stand out, make sure you've researched:

  • → Glydways' technical approach—dedicated roadways, standardized vehicles, 24/7 operation—and how it differs from other autonomous transit systems
  • → Their mission statement and recent announcements to understand their growth stage and priorities
  • → The autonomy engineering team's focus on coordination, orchestration, and task scheduling (mentioned in job description)
  • → Net-negative GHG transportation concepts and how fleet optimization contributes to environmental goals

💬 Prepare for These Interview Topics

Based on this role, you may be asked about:

1 Walk through how you would model Glydways' fleet coordination problem as an optimization formulation (likely MILP or QP)
2 Discuss trade-offs between centralized planning vs. distributed decision-making for autonomous vehicle orchestration
3 Explain how you'd handle real-time disruptions (e.g., vehicle breakdowns, demand spikes) using MPC or similar methods
4 Describe your experience with queueing systems and how you'd apply them to balance vehicle supply with passenger demand
5 Code review or whiteboard exercise involving numerically stable implementation of an optimization algorithm in Python or C++
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⚠️ Common Mistakes to Avoid

  • Presenting optimization experience as purely theoretical without real-world application examples
  • Failing to connect your skills to Glydways' specific model (e.g., discussing general ride-sharing algorithms without addressing dedicated roadway constraints)
  • Overemphasizing machine learning or perception skills when the role focuses on optimization, control, and resource allocation

📅 Application Timeline

This position is open until filled. However, we recommend applying as soon as possible as roles at mission-driven organizations tend to fill quickly.

Typical hiring timeline:

1

Application Review

1-2 weeks

2

Initial Screening

Phone call or written assessment

3

Interviews

1-2 rounds, usually virtual

✓

Offer

Congratulations!

Ready to Apply?

Good luck with your application to Glydways!

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