Staff/Principal AI Transformation Engineer

DiDi•San Jose, CA

About The Position

DiDi Autonomous Driving is seeking a visionary, highly technical, and mission-driven Staff or Principal AI Transformation Engineer to lead company-wide AI transformation. The role will help evolve the traditional R&D organization into an AI-Native organization by combining Large Language Model expertise, systems architecture, and enterprise-scale AI deployment. The engineer will work closely with core engineering teams and translate AI capabilities into production-ready systems. The role owns the architecture of a unified, distributed AI platform spanning data processing, model training, inference, and evaluation. It includes evaluating and integrating frontier LLM ecosystems and commercial AI platforms; deploying resilient LLMOps/MLOps automation and feedback pipelines; optimizing GPU cluster utilization, inference latency, and throughput; and connecting external technology advances to the company’s internal systems and technical roadmap.

Requirements

Demonstrated ability to design complex system-level architectures while writing core code, debugging deep system issues, and optimizing low-level execution paths.
Proficiency in core programming languages such as C++, Python, Java, or JavaScript.
Ability to build technical authority, align priorities, and drive diverse engineering teams—including Algorithms, Infrastructure, and Hardware—toward an AI-first engineering paradigm without formal administrative authority.
Experience leading or significantly contributing to a large-scale corporate AI-native transformation, or building enterprise-grade AI/ML platforms from 0 to 1.
Hands-on deployment, tuning, and optimization experience with AI infrastructure tools and frameworks such as PyTorch, Ray, vLLM, Triton Inference Server, Kubernetes, DeepSpeed, and Megatron-LM.
Deep practical experience in distributed LLM training and inference optimization, and large-scale compute cluster infrastructure and operations.

Nice To Haves

Familiarity with autonomous driving algorithms, including Perception, Planning, Control, or Simulation; robotics; physics-based simulation engines; or ultra-large-scale ML training and serving clusters.
8–10+ years of professional engineering experience in systems software, core cloud infrastructure, or production-grade machine learning platforms.
Hands-on experience building custom AI Copilot applications, autonomous multi-agent frameworks, or high-tier developer productivity platforms.
A record of delivering core projects amid complex business logic, fast-paced or high-pressure environments, or mission-critical systems.
Active contribution to the broader technology community, such as maintaining or owning open-source projects, writing technical blogs or papers, or speaking at leading AI/ML conferences.

Responsibilities

Evaluate, select, and deeply integrate frontier LLM ecosystems and commercial AI platforms.
Design the architecture of a unified, distributed AI platform covering data processing, model training, inference pipelines, and evaluation frameworks.
Embed with autonomous driving teams across Perception, Prediction, Planning & Control, and Simulation to identify engineering bottlenecks and remove friction.
Translate AI capabilities into production-ready internal ecosystems, such as AI DevOps and AI Copilots.
Design and implement resilient, scalable automation pipelines for LLM deployment, monitoring, and continuous feedback.
Optimize GPU cluster utilization, inference latency, and throughput in large-scale production environments.
Track developments in AGI and systems engineering and connect external technology innovations with internal systems.
Help maintain a 1–3 year competitive edge in AI infrastructure.