Sr. / Staff Software Engineer, Infrastructure (Autonomy)

DiDi•San Jose, CA

About The Position

DiDi Autonomous Driving is seeking an experienced Senior or Staff Software Engineer, Infrastructure, to lead the architecture and evolution of the core platform powering its autonomous driving software stack. The role defines the technical roadmap for high-performance middleware, custom compiler infrastructure (including ccatch), onboard toolchains, and large-scale simulation frameworks such as ezsim and automated crash triage. The engineer will work closely with Autonomy and Simulation teams to strengthen platform stability, establish engineering standards, and improve developer productivity across teams. The position leads the design and implementation of low-latency, high-throughput onboard systems and runtime execution infrastructure; owns build tools and compiler execution strategies; and develops deterministic simulation and diagnostic platforms. It also involves performance and memory optimization on embedded hardware, advising onboard teams on infrastructure design, and mentoring engineers while setting architectural and engineering standards.

Requirements

Bachelor’s degree or higher 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 technical leadership and system architecture experience, including driving complex, multi-team engineering initiatives from inception through production deployment.
Expert-level understanding of C++, system programming, multithreading, concurrency models, and low-latency IPC/middleware architectures.
Deep knowledge of compiler internals such as Clang/GCC and LLVM; build systems such as Bazel/CMake; and compilation optimization tools, including caching systems such as ccatch.
Demonstrated ability to debug complex system-level faults, kernel/user-space crashes, memory corruption, and race conditions.

Nice To Haves

Experience architecting or extending core simulation execution platforms such as ezsim, evaluation frameworks, and automated crash-triage pipelines for robotics or autonomous driving.
Expertise in embedded systems, cross-compilation target setups such as QNX, Linux RT, and NVIDIA Orin, and hardware acceleration layer integrations.
Experience with low-level systems profiling tools such as eBPF or gperf, customized memory allocators, or custom IPC protocol design.
History of building developer productivity tools, developing quantitative software evaluation metrics, and driving root-cause analysis in complex robotic environments.

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 build infrastructure, toolchains, and compiler execution strategies, including ccatch and distributed build caching, to improve developer velocity and software deployment pipelines.
Architect robust, deterministic simulation platforms such as ezsim and develop automated post-mortem diagnostic toolchains for simulator crash triage, core dump analysis, and systemic fault isolation.
Drive cross-functional performance profiling, memory optimization, and latency reductions across the autonomous driving software stack on embedded hardware platforms.
Guide onboard teams in software design to maximize efficiency, flexibility, scalability, and reliability across the autonomy stack.
Improve internal development tools, diagnostic systems, and engineering workflows while maintaining system complexity and runtime stability.
Mentor engineers, set engineering standards, lead technical design reviews, and establish architecture guidelines across cross-functional teams.