Software Engineer, Motion Planning

DiDi; San Jose (CA); On-site; USD 141,000 - 235,000; Full time

Job summary

DiDi Autonomous Driving is seeking a Software Engineer / Sr. Software Engineer to advance core decision-making and motion planning systems for autonomous vehicles.

You will build algorithms enabling safe, smooth navigation across complex environments, spanning high-level behavior to low-level trajectory optimization. The role emphasizes collaboration with Prediction, Perception, and Control teams and requires strong C++ skills and robotics fundamentals to deliver robust world models and safe

Qualifications

• 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++ for real-time algorithms.
• Robotics fundamentals: decision-making, motion planning, control theory, and optimization.
• Experience in motion planning, trajectory optimization, world environment reasoning, and cost design.

Responsibilities

• Design and implement the core motion planning logic for high-level actions such as lane changes and merges.
• Develop and optimize motion planning algorithms that integrate geometry reasoning and speed profiling into a cohesive trajectory.
• Architect and enhance the geometry system for generating feasible, compliant paths.
• Refine the velocity system for context-aware, comfortable, and safe velocity profiles.
• Model complex driving scenarios and agent interactions to build a robust world model for the behavioral planner.
• Design costs for trajectory ranking to balance ETAs, comfort, and safety.
• Conduct in-depth analysis, testing, and debugging across scenarios and lead root-cause investigations.
• Collaborate with Prediction, Perception, and Control teams to ensure smooth data flow from understanding to motion.

Skills

C++

Motion planning

Trajectory optimization

Optimization

Robotics fundamentals

Education

B.S./M.S. in Computer Science, Robotics, or related field

PhD or internship in robotics planning systems

Job description

About The Company

DiDi's autonomous driving unit was established in 2016 with the mission of developing Level 4 autonomous driving (AD) technology to make transportation safer and more efficient. In August 2019, the unit became an independent company, DiDi Autonomous Driving, dedicated to advanced AD R&D, product application, and business expansion. We believe integrating AD technology into a shared-mobility fleet will generate immense social value. By leveraging DiDi's specialized technology, operational expertise, and integrated ecosystem, we are positioned to build and operate a highly efficient, user-oriented autonomous fleet.

About The Company

DiDi's autonomous driving unit was established in 2016 with the mission of developing Level 4 autonomous driving (AD) technology to make transportation safer and more efficient. In August 2019, the unit became an independent company, DiDi Autonomous Driving, dedicated to advanced AD R&D, product application, and business expansion. We believe integrating AD technology into a shared-mobility fleet will generate immense social value. By leveraging DiDi's specialized technology, operational expertise, and integrated ecosystem, we are positioned to build and operate a highly efficient, user-oriented autonomous fleet.

About The Role

We are seeking a Software Engineer /Sr. 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.

Responsibilities

• Design and implement the core motion 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.

Qualifications

• 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++ 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: motion planning, trajectory optimization and world environment reasoning, trajectory ranking and cost design.

Preferred Qualifications

• 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.

The base salary range for the Software Engineer position is $141,463–$235,182 annually, and for the Senior Software Engineer position is $169,783–$282,264 annually, in addition to bonus, equity, and benefits. Our salary ranges are determined by the role, level, and location. Within the applicable range, individual compensation is based on the work location as well as other factors, including job‑related skills, experience, and relevant education and training.

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