Software Engineer – Motion & Behavioral Planning (post end 12/15)

DiDi Labs•San Jose, CA

3h•$129,189 - $214,776

About The Position

We are seeking a junior or skilled Software Engineer to join our team and develop the core decision-making and motion planning systems for our autonomous vehicles. In this role, you will be responsible for creating the algorithms that enable smooth, safe, and intelligent navigation in complex environments. You will tackle challenges across the full motion planning stack, from high-level behavioral reasoning to low-level trajectory optimization.

Requirements

B.S./M.S. in Computer Science, Robotics, or a related field.
Experience in autonomous systems, robotics, or automotive software development.
Strong proficiency in C++ and Python for implementing complex, real-time algorithms.
Solid understanding of robotics fundamentals, including decision-making, motion planning, control theory, trajectory ranking, search and optimization algorithms etc.
Related experience in one or more of the following: behavioral planning, motion planning, behavior and world environment reasoning, trajectory ranking and cost design.

Nice To Haves

PhD or internship experience related to robotics planning system designs.
Knowledge of vehicle dynamics and longitudinal/lateral control systems.
Solid understanding of machine learning principles, reinforcement learning and related algorithms.

Responsibilities

Design and implement the core Behavioral Planning logic that determines the vehicle's high-level actions (e.g., lane changes, merges, yields, and interactions with other agents).
Develop and optimize the motion planning algorithms that execute behavioral decisions, integrating Geometry Reasoning (path) and Speed Reasoning (velocity) into a cohesive trajectory.
Architect and enhance the geometry system for generating geometrically feasible and compliant paths.
Architect and refine the velocity system for generating context-aware, comfortable, and safe velocity profiles.
Model complex driving scenarios and agent interactions to create a robust world model for the behavioral planner.
Design different costs for trajectory ranking to trade off ETAs, comfort and safety of the vehicle behaviors.
Conduct in-depth analysis, testing, and debugging of the system's performance in various scenarios, leading root cause investigations.
Collaborate with Prediction, Perception, and Control teams to ensure a seamless flow from environmental understanding to physical vehicle motion.