The Best Way to Move People High-capacity, on-demand, and affordable mobility

Software Engineering Intern – Dispatch, Fleet Optimization

Full-stack EngineerSoftware EngineerInternshipRemoteTeam 51-200Since 2016H1B SponsorCompany Site LinkedIn

Location

United States

Posted

49 days ago

Salary

Not specified

Bachelor DegreeEnglishLinuxPythonPy TorchTensorflow

Job Description

• 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.

Job 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.