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Senior Software Engineer – Autonomy – Behavior – Planning & Controls

Senior Software Engineer – Autonomy – Behavior – Planning & Controls

CompanyCyngyn
LocationMountain View, CA, USA
Salary$190000 – $220000
TypeFull-Time
DegreesMaster’s, PhD
Experience LevelSenior

Requirements

  • MS/PhD in Computer Science, Robotics, or a related technical field, or equivalent practical experience.
  • Strong proficiency in C++ and Python with a deep understanding of software development best practices.
  • Solid foundation in robotics principles, particularly motion planning and control systems.
  • Strong background in vehicle dynamics and experience applying these principles to autonomous systems.
  • Knowledge of nonlinear optimization and search-based planning techniques.
  • Familiarity with ROS2 for designing, building, and operating robotic systems.
  • Strong mathematical foundation, including geometry, linear algebra, and probability.
  • Excellent problem-solving abilities with a proactive approach in a fast-paced, collaborative environment.
  • Strong communication and cross-team collaboration skills.

Responsibilities

  • Design, implement, and optimize motion planning and control algorithms for autonomous vehicles.
  • Develop and enhance software infrastructure for vehicle simulation, performance validation, and system diagnostics.
  • Architect and refine path-planning and tracking control algorithms to improve autonomy performance.
  • Collaborate with cross-functional teams to deploy and validate solutions in both simulated and real-world environments.
  • Balance hands-on development, code reviews, and research to achieve product-driven milestones in a fast-paced startup environment.
  • Integrate cutting-edge robotics research into our autonomous driving stack to enhance system capabilities.

Preferred Qualifications

  • Experience implementing controllers and planners for real-time safety-critical mobile autonomous systems.
  • Experience with simulation environments and developing kinematic/dynamic models for autonomous vehicles.
  • Exposure to machine learning, deep learning, and physics-based foundation models.