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DiDi Labs

Software Engineer – Map Fusion & Planning

San Jose, CA$141.5k–$235.2kmidAdded today

About this role

DiDi Autonomous Driving seeks a Software Engineer to develop map fusion and motion planning systems for Level 4 autonomous vehicles. You'll design scalable infrastructure integrating HD maps, real-time sensor perception, and trajectory generation while optimizing deep learning models from training through efficient C++ runtime deployment.

What you'll do

  • Architect data pipelines and APIs for map fusion, real-time vectorization, and motion planning modules
  • Integrate offline HD maps with online perception to create unified local environment models
  • Implement DETR-style, query-based vector decoding networks in bird's-eye-view for map element generation
  • Design and validate motion planning algorithms with feedback loops between mapped features and trajectory optimization
  • Own end-to-end deployment of deep learning models from Python training through ONNX to C++ runtime execution
  • Develop real-time map anomaly detection and safety validation systems for planning reliability

What they're looking for

  • C++ (performance-critical systems)
  • Python (model training and development)
  • Deep learning frameworks (PyTorch, TensorFlow)
  • ONNX optimization and model deployment
  • Motion planning and trajectory optimization algorithms
  • DETR and transformer-based architectures
  • Real-time systems and multi-threaded programming
  • Autonomous driving and robotics fundamentals
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DiDi Labs

DiDi Labs develops autonomous driving technology for Level 4 vehicles, focusing on motion planning, behavioral decision-making, map fusion, and robotic simulation systems. The company is hiring software engineers and researchers to build algorithms, trajectory optimization systems, and cloud-based simulation infrastructure that enable safe and intelligent autonomous vehicle navigation.

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Likely interview questions

  • Describe your experience integrating offline HD maps with real-time sensor perception data. What challenges did you face?
  • How would you design the data pipeline architecture to ensure low-latency communication between map fusion, perception, and planning modules?
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