--- title: 'Sr. / Staff Software Engineer, Infrastructure (Autonomy) at DiDi Labs' canonical: 'https://feeny.ai/job/sr-staff-software-engineer-infrastructure-autonomy-didi-labs-san-jose-fgb84pk2gbty' type: 'job' last_seen: '2026-09-09' --- # Sr. / Staff Software Engineer, Infrastructure (Autonomy) at DiDi Labs - **Company:** DiDi Labs - **Location:** San Jose, CA - **Posted:** 2026-08-17 - **Last confirmed live:** 2026-09-09 - **Apply:** https://job-boards.greenhouse.io/didi/jobs/8131891 ## 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 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 DiDi Labs ## Company Overview - **One-liner**: DiDi Labs is the U.S. research and development arm of DiDi, focused on advancing Level 4 autonomous driving technology through large-scale AI models and real-world ride-hailing data. - **Entity Type**: Private (subsidiary of DiDi Global Inc., which is not publicly traded) - **Headquarters**: Mountain View, California, United States - **Founded**: 2017 (DiDi Labs); parent company Didi Chuxing founded in 2012 - **Founders**: DiDi Labs was launched under the leadership of Cheng Wei (DiDi founder and CEO) and initially led by Dr. Fengmin Gong, Vice President of DiDi Research Institute. ## Core Business - **Primary Industry**: Autonomous driving (Level 4), artificial intelligence, mobility technology - **Target Customers**: B2B (integrated into DiDi’s shared-mobility fleet), enterprise partnerships (e.g., automotive OEMs, cities for smart transportation) - **Mission**: Develop Level 4 autonomous driving technology to make transportation safer and more efficient, and integrate it into a shared-mobility fleet to generate broad social value. ## Products & Services - **VLA (Vision-Language-Action) Foundation Models**: Large-scale deep learning systems for multi-agent behavior prediction and autonomous vehicle planning, built on DiDi’s massive real-world driving dataset. - **Behavioral & Motion Planning Systems**: Core decision-making and trajectory optimization algorithms that enable safe, smooth navigation in complex urban environments. - **AI Research (Computer Vision, NLP, Speech, Mapping)**: Through DiDi AI Labs (China-based but collaborative), the company develops speech interaction, smart mapping, and perception solutions. - **Autonomous Vehicle Testing & Fleet Operations**: DiDi Autonomous Driving operates test fleets in the United States and China, and runs open-source challenges (e.g., Udacity Self-Driving Car Challenge). ## Market Standing - **Valuation**: Not publicly disclosed for DiDi Labs; parent DiDi Global was valued at over $60 billion during its 2021 IPO (now delisted and private). - **Key Metric**: Total funding for DiDi exceeds $20 billion across multiple rounds (SoftBank, Tencent, etc.). DiDi Labs benefits from parent’s resources. - **Notable Investors/Partners**: DiDi’s investors include SoftBank Vision Fund, Tencent, Uber, Apple, and others. DiDi Labs partners with Udacity, and its team includes security expert Charlie Miller. - **Growth Signals**: Independent autonomous driving unit established in 2019; active hiring in 2025 for VLA and motion planning roles; integration with DiDi’s massive ride-hailing network (over 500 million users) provides unmatched real-world data. ## Competitive Advantages - **Massive Real-World Data**: Access to DiDi’s billions of trips across Asia, Latin America, and Australia gives the team proprietary training data for behavior prediction and planning. - **Integration with Shared Mobility**: Autonomous fleet can be deployed directly into an existing ride-hailing platform, offering a clear go-to-market path. - **Top Talent & Research**: Deep roster of AI researchers, published in top conferences (NeurIPS, CVPR, ICLR, etc.), and strong collaboration with global research network. - **Open Innovation**: Hosts open-source challenges and engages with the broader self-driving community. ## Strategic Focus - **Current Priorities**: Scaling VLA foundation models for multi-agent prediction and planning; deploying Level 4 autonomous vehicles in operational domains; optimizing latency and safety for production deployment. - **Direction for Growth**: Expanding the autonomous fleet, deepening partnerships with cities, and building the “smart transportation ecosystem” through AI-driven infrastructure. ## Why Work Here - **Cutting-Edge AI at Scale**: Work on large-scale distributed training (hundreds to thousands of GPUs), transformers, diffusion models, and real-time planning for safety-critical systems. - **Impact**: Directly contribute to autonomous driving technology that could transform urban mobility and reduce traffic accidents. - **Compensation & Benefits**: Competitive base salary (e.g., $129k–$247k for ML Engineer, plus bonus, equity, and benefits). Roles in Mountain View with hybrid/office expectations. - **Culture & Perks**: Collaboration with world-class researchers, access to massive datasets, and a mission-driven environment focused on safety and innovation. - **Work Model**: Based in Mountain View, CA; roles are likely on-site or hybrid given the hands-on nature of autonomous vehicle development. ## Sources 1. [Job Board - Machine Learning Engineer, VLA](https://job-boards.greenhouse.io/didi/jobs/7975368) 2. [Job Board - Software Engineer, Motion & Behavioral Planning](https://job-boards.greenhouse.io/didi/jobs/7975371) 3. [PRNewswire - DiDi Launches U.S. Labs in Silicon Valley](https://www.prnewswire.com/news-releases/didi-launches-us-labs-in-silicon-valley-to-build-global-nexus-of-innovation-300420435.html) 4. [Golden - DiDi Labs Company Profile](https://golden.com/wiki/DiDi_Labs-GE84KD8) 5. [DiDi Official - AI Labs Page](https://www.didiglobal.com/science/ailabs) ## Other roles at DiDi Labs - [Software Engineer / Sr. Software Engineer, Planning Selection Autonomy](https://feeny.ai/job/software-engineer-sr-software-engineer-planning-selection-autonomy-didi-labs-znt50prkgrw8) — San Jose, CA - [Software Engineer, Motion Planning](https://feeny.ai/job/software-engineer-motion-planning-didi-labs-san-jose-gj5n60m2bt8x) — San Jose, CA - [Sr. /Staff System Safety Engineer, Autonomy](https://feeny.ai/job/sr-staff-system-safety-engineer-autonomy-didi-labs-san-jose-hr27mnazva4n) — San Jose, CA - [Staff/Principal AI Transformation Engineer](https://feeny.ai/job/staff-principal-ai-transformation-engineer-didi-labs-san-jose-0kt7qqpjbfaz) — San Jose, CA - [Technical Program Manager, Autonomy](https://feeny.ai/job/technical-program-manager-autonomy-didi-labs-san-jose-qqr86j3cpk46) — San Jose, CA - [Software Engineer, Map Fusion & Planning](https://feeny.ai/job/software-engineer-map-fusion-planning-didi-labs-san-jose-g8fe119htkz6) — San Jose, CA - [Motion Planning Engineer (PhD, Intern)](https://feeny.ai/job/motion-planning-engineer-phd-intern-didi-labs-san-jose-md1kry2qmm4c) — San Jose, CA - [Senior/Sr. Staff AI Infrastructure Engineer, Inference & Optimization](https://feeny.ai/job/senior-sr-staff-ai-infrastructure-engineer-inference-optimization-didi-labs-san-09dry78v6mty) — San Jose, CA