Software Engineer – Map Fusion & Planning
Didi•4h ago
United StatesHybridFull-timeMid Level3+ yrs exp
Visa-friendly
Top focus
Software EngineerSoftware Engineer IiSenior Software Engineer
- 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
- 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
- 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 / Senior Software Engineer to develop the next-generation map fusion and motion planning systems for our autonomous vehicles. In this role, you will bridge the gap between semantic HD maps, real-time sensor perception
- vehicle trajectory generation. You will design scalable software infrastructure, implement advanced geometric and deep learning frameworks
- develop the planning algorithms that enable our vehicles to navigate complex, dynamic environments safely and predictably.
- Responsibilities
- System Architecture : Architect the data flow pipelines and APIs for map fusion, real-time map vectorization, and behavior/motion planning modules.
- Algorithm Deployment : Design and deploy robust software frameworks that integrate offline High-Definition (HD) maps with online perception data to create a unified local environment model.
- Advanced Mapping Networks : Implement and optimize state-of-the-art networks utilizing DETR-style, query-based vector decoding in bird's-eye-view (BEV) for online map element generation.
- Motion Planning & Optimization : Design, implement, and validate core motion planning algorithms, establishing a tight feedback loop between vectorized map features, path generation, and trajectory optimization.
- Model Deployment Pipelines : Own the end-to-end deployment pipeline for deep learning mapping models—from Python-based training and ONNX optimization to highly efficient runtime execution in C++.
- Safety & Anomaly Detection : Develop real-time map anomaly and scene-change detection algorithms to ensure planning system reliability under varying or outdated map conditions.
- Performance Optimization : Optimize system latency, CPU/GPU memory footprint, and multi-threaded execution of safety-critical C++ modules.
- Qualifications
- Education: B.S./M.S. or Ph.D. in Computer Science, Robotics, Electrical Engineering, or a related field.
- Experience: 3+ years (Software Engineer) / 5+ years (Senior Software Engineer) of experience in autonomous driving, robotics architecture, or spatial computing.
- Software Mastery: Expert proficiency in production-grade C++ (Modern C++14/17/20, multi-threading, memory management) and strong prototyping proficiency in Python.
- Motion Planning Fundamentals: Robust foundational knowledge in path planning (e.g., A*, Dijkstra, Hybrid A*, sampling-based planners like RRT*) and kinematic/dynamic vehicle models.
- Robotics Core: Deep understanding of robotics fundamentals, including coordinate transformations, spatial geometry, and state estimation.
- System Design: Strong system design skills with a solid understanding of middleware (e.g., ROS2, DDS) and distributed software architectures.
- Preferred Qualifications
- Trajectory Optimization: Hands-on experience with numerical trajectory optimization methods (e.g., MPC, QP/Nonlinear optimization, interior-point methods) and optimization solvers (e.g., OSQP, Ipopt, Ceres Solver).
- Advanced Mapping Experience: Hands-on experience working with HD map formats (Lanelet2, OpenDRIVE) and modern end-to-end learning frameworks (e.g., MapTR, VectorNet) that leverage query-based BEV perception.
- Deep Learning Runtime & Deployment: Proven track record of exporting complex deep learning architectures via ONNX and deploying them into real-time C++ production environments using TensorRT.
- Anomaly Detection: Proven track record of developing algorithms for map anomaly detection, sensor-to-map misalignments, or online scene-change identification.
- Safety-Critical Systems: Knowledge of real-time operating systems (RTOS), deterministic software execution, and safety-critical software design principles.
- The base salary range for this full-time position is $129,189-$214,776 annually in addition to bonus, equity and benefits. Our salary ranges are determined by role, level
- location. Within the range, individual pay is determined by work location and additional factors, including job-related skills, experience
- relevant education or training.
- I acknowledge that prior to submitting this application, I have read and accepted the Privacy Notice for California Residents which is available on https://v.didi.cn/AQnxlBa
Required skills
C++PythonRoboticsMotion PlanningDeep LearningSpatial ComputingSystem Design