Autonomous Driving Systems Project Manager

Deeproute.aiFremont, CA
$130,000

About The Position

Lead end-to-end project management for the development of autonomous vehicle planning modules, working closely with cross-functional teams, and testing to ensure timely and quality-driven execution of project deliverables. Define and oversee the technical roadmap and execution plans for key planning functionalities, including rare-scenario handling, constraint-aware motion planning, and overall system robustness, leveraging deep technical understanding of autonomous driving software systems. Manage the full project lifecycle, including requirements definition, milestone planning, resource coordination, risk identification and mitigation, technical documentation, and regular status reporting to stakeholders and senior leadership. Contribute to platform architecture and integration strategy, coordinating the deployment of planning algorithms into both simulation platforms and real-world driving environments, ensuring alignment with system-level goals and performance KPIs.

Requirements

  • Master’s degree in Mechanical Engineering, or a related field
  • At least 2 years’ experience in algorithms planning and software systems within the autonomous driving industry including:
  • Develop and test planning systems
  • Execute software development lifecycles
  • Integrate in real-time systems
  • Optimize motion-planning performance and robustness
  • Proficiency in C++, Python, Machine Learning, Git, simulation tooling

Responsibilities

  • Lead end-to-end project management for the development of autonomous vehicle planning modules
  • Define and oversee the technical roadmap and execution plans for key planning functionalities
  • Manage the full project lifecycle, including requirements definition, milestone planning, resource coordination, risk identification and mitigation, technical documentation, and regular status reporting to stakeholders and senior leadership
  • Contribute to platform architecture and integration strategy, coordinating the deployment of planning algorithms into both simulation platforms and real-world driving environments
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