Sr. Software Engineer, Fleet Dispatch

This is a remote position.

Sr. Software Engineer, Fleet Dispatch  - Remote Job, 3-5+ Year Experience

Annual Income: $99K - $122K

About us: Patterned Learning is a platform that aims to help developers code faster and more efficiently. It offers features such as collaborative coding, real-time multiplayer editing, and the ability to build, test, and deploy directly from the browser. The platform also provides tightly integrated code generation, editing, and output capabilities.

Responsibilities

• Design and build a scalable, efficient, and robust robotaxis dispatch service.
  

• Craft and refine algorithms for real-time dispatch optimization and route planning, leveraging operational research techniques.
  

• Apply operational research methods such as linear programming, integer programming, stochastic processes, and queuing theory to solve complex dispatch problems.
  

• Use operational research tools for data analysis, forecasting demand, and modeling transport networks to improve robotaxi service efficiency.
  

• Integrate optimization algorithms into the robotaxi platform.
  

• Build and use in-house or 3rd-party simulation models to test dispatch optimization algorithms’ effectiveness.
  

• Conduct detailed operational research studies to continually assess and enhance the dispatch system’s performance.
  

• Stay updated with the latest trends in operational research, autonomous vehicles, and AI, applying cutting-edge research to our dispatch systems.
  

• Collaborate with teams of world-class engineers, product managers, technical program managers from diverse backgrounds with expertise in distributed computing, rider experience, web and mobile applications, autonomous driving, mapping, AI, and software infrastructure to understand their needs and incorporate operational research insights into solutions.
  
  

Qualification

• Bachelor’s/Master’s degree in Computer Science, Engineering, Mathematics, Operations Research, or related field.
  

• Proven experience in algorithm development, with a strong emphasis on operational research methodologies.
  

• Proficiency in programming languages like Python, Java or Kotlin, or C++.
  

• Solid background in machine learning, AI, operational research, and data analysis.
  

• Passion for learning new technologies, domains, and challenging the status quo.
  

• Strong mathematics skills.
  

• Exceptional problem-solving, analytical, and quantitative skills.
  
  

Bonus Qualification

• PhD in Operations Research, Computer Science, or a related field with a focus on optimization algorithms or AI.
  

• Experience with GIS and mapping software.
  

• Experience in transportation, logistics, or autonomous vehicles.
  

• Experience in building/using fleet simulation tools.
  

• Ability to lead through ambiguity and work with diverse stakeholders.
  

• Ability to Iterate quickly and efficiently on building end-to-end experiences.
  

• Experience handling large data sets and scaling service horizontally to a large number of users.
  

• Experience developing cloud services and an understanding of design for scalability, performance and reliability.
  

• Knowledge about microservice design patterns and DB access.
  

• Knowledge of engineering practices and patterns for the full software development life cycle, including coding standards, code reviews, source control management, build processes, testing, certification, and live site operations.
  

Why Patterned Learning LLC?

Patterned Learning can provide intelligent suggestions, automate repetitive tasks, and assist developers in writing code more effectively. This can help reduce coding errors, improve productivity, and accelerate the development process.

The pattern recognition is particularly relevant in the context of coding. Neural networks, especially deep learning models, are commonly employed for pattern detection and classification tasks. These models simulate human decision-making and can identify patterns in data, making them well-suited for tasks like code analysis and generation.