Careers at DiDi

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 an experienced and mission-driven Senior/Sr. Staff AI Infrastructure Engineer , Inference & Optimization to lead the performance tuning, deployment, and resource scheduling of cutting-edge AI models across on-vehicle and cloud infrastructure. In this role, you will design high-efficiency inference pipelines, build system-level stability frameworks, and optimize hardware execution to ensure ultra-low latency and rock-solid operational reliability. You will act as a technical leader in AI infrastructure, accelerating model iteration and bridging the gap between frontier deep learning algorithms and real-time autonomous systems. Responsibilities Own the deployment, optimization, and resource scheduling of vehicle-side AI models, ensuring high efficiency, low latency, and robust execution within embedded constraints. Lead vehicle-side system stability initiatives, conducting independent root-cause analysis and driving resolution for complex, system-level performance bottlenecks and runtime anomalies. Architect and scale service-oriented deployment environments for Large Language Models (LLMs) and foundational models to support offline simulation, automated annotation, and rapid model validation. Track and evaluate cutting-edge industry methodologies, continuously integrating advanced optimization toolchains, quantization techniques, and execution engines. Establish system-level profiling and telemetry frameworks using CUDA tools to monitor, analyze, and maximize hardware utilization across target GPU architectures. Collaborate cross-functionally with Autonomous Driving Perception/Prediction, Cloud Infrastructure, and Safety teams to enable rapid algorithm iteration and scalable vehicle deployment. Qualifications Master’s or higher degree in Computer Science, Software Engineering, Systems Engineering, or a closely related technical field. 3-8+ years of industry experience in high-performance computing, AI infrastructure, model optimization, or embedded deployment. Strong proficiency in C++ and Python, with solid expertise in parallel programming (CUDA, OpenMP) and low-level system profiling tools. Deep familiarity with mainstream inference engines (e.g., TensorRT, ONNX Runtime) and specialized LLM inference/serving frameworks (e.g., vLLM, SGLang, TensorRT-LLM). Practical understanding of modern GPU hardware architectures (e.g., NVIDIA Hopper, Thor) and memory bandwidth management. Demonstrated ability to diagnose complex software-hardware integration issues and drive scalable, production-grade solutions. Preferred Qualifications Hands-on experience optimizing and deploying AI models on the NVIDIA Thor platform, including hardware resource scheduling and acceleration. Proven track record of serving large foundation models (e.g., LLaMA, Qwen, GPT) in production or high-throughput cloud pipelines using frameworks like vLLM, SGLang, TGI, or LightLLM. Background in deep learning training frameworks (PyTorch) and practical experience with model quantization (INT8/FP8/AWQ), kernel fusion, or graph compilation. Experience deploying real-time, high-availability AI workloads in autonomous vehicles, robotics, or edge devices. The base salary range for this full-time position is $169,783 - $351,000 annually in addition to bonus, equity and benefits. Our salary ranges are determined by role, level, and location. Within the range, individual pay is determined by work location and additional factors, including job-related skills, experience, and 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

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 an experienced Sr. Software Engineer / Staff Software Engineer, Infrastructure to lead the architecture and evolution of the core platform powering our autonomous driving software stack. In this role, you will define the technical roadmap for our high-performance middleware, custom compiler infrastructure (including ccatch), onboard toolchains, and large-scale simulation frameworks (ezsim and automated crash triage). As a technical leader, you will collaborate closely with Autonomy and Simulation teams to maximize platform stability, enforce systemic engineering standards, and accelerate cross-team developer productivity at scale. Responsibilities Lead the architectural vision, design, and implementation of next-generation, low-latency, high-throughput IPC messaging middleware and runtime execution engines for onboard systems. Own end-to-end build infrastructure, toolchains, and compiler execution strategies (including ccatch and distributed build caching) to optimize developer velocity and software deployment pipelines. Architect robust, deterministic simulation platforms (ezsim) and build automated post-mortem diagnostic toolchains for rapid simulator crash triaging, core dump analysis, and systemic fault isolation. Drive cross-functional performance profiling, memory optimization, and latency reductions across the full autonomous driving software stack on embedded hardware platforms. Act as a crucial infrastructure domain expert for onboard teams, guiding software design for maximum efficiency, flexibility, scalability, and reliability across our evolving autonomy stack. Continuously elevate internal development tools, diagnostic systems, and engineering workflows to maximize developer velocity while maintaining tight control over system complexity and runtime stability. Technical leadership: Mentor engineers, set high engineering standards, lead technical design reviews, and establish foundational architecture guidelines across cross-functional teams. Qualifications Bachelor’s or higher degree in Computer Science, Computer Engineering, Software Engineering, or a closely related technical field. 6–10+ years of software engineering experience designing, architecting, and maintaining complex real-time systems, Linux systems infrastructure, or autonomous driving platforms in C++. Proven track record of technical leadership, system architecture, and driving complex, multi-team engineering initiatives from inception to production deployment. Expert-level understanding of C++, system programming, multi-threading, concurrency models, and low-latency IPC/middleware architectures. Deep knowledge of compiler internals (Clang/GCC, LLVM), build systems (Bazel/CMake), and compilation optimization tools (including caching systems like ccatch). Proven capability in debugging complex system-level faults, kernel/user-space crashes, memory corruption, and race conditions. Preferred Qualifications Prior experience architecting or extending core simulation execution platforms (ezsim), evaluation frameworks, and automated crash triaging pipelines for robotics or autonomous driving. Recognized domain expertise in embedded systems, cross-compilation target setups (e.g., QNX, Linux RT, NVIDIA Orin), and hardware acceleration layer integrations. Deep experience in low-level systems profiling (e.g., eBPF, gperf), customized memory allocators, or custom IPC protocol design. Demonstrated history of building developer productivity tools, developing quantitative software evaluation metrics, and driving root-cause analysis in complex robotic environments. The base salary range for this full-time position is $169,783 - $338,694 annually in addition to bonus, equity and benefits. Our salary ranges are determined by role, level, and location. Within the range, individual pay is determined by work location and additional factors, including job-related skills, experience, and 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

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 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. 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. Qualifications B.S./M.S. in Computer Science, Robotics, or a related field. E xperience 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 . 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 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, and location. Within the range, individual pay is determined by work location and additional factors, including job-related skills, experience, and 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

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. 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