Staff/Principal AI Transformation Engineer

DiDi Labs•San Jose, CA

6d•$255,000 - $351,000

About The Position

DiDi Autonomous Driving is seeking a visionary, highly technical, mission-driven Staff or Principal AI Transformation Engineer to lead the company-wide transition toward an AI-Native architecture. The role combines LLM expertise, systems architecture, and enterprise AI scaling to help evolve the R&D organization into an AI-Native engineering organization. The engineer will design and help operate a unified distributed AI platform covering data processing, model training, inference, and evaluation. Working directly with autonomous driving engineering teams, the role identifies bottlenecks and turns AI capabilities into production-ready internal systems. It also advances LLMOps and MLOps automation, improves GPU utilization and inference performance, and connects emerging external technologies to internal infrastructure and long-term technical strategy.

Requirements

Demonstrated technical leadership designing complex system-level architectures while remaining hands-on with core code, deep system debugging, and low-level execution optimization.
Proficiency in core programming languages such as C++, Python, Java, or JavaScript.
Demonstrated ability to build technical authority, align priorities, and influence diverse engineering teams without formal administrative authority.
Proven experience leading or substantially 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, including 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, and 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.
Proven success 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 open-source projects, authoring technical blogs or papers, or speaking at premier AI/ML conferences.

Responsibilities

Evaluate, select, and deeply integrate frontier LLM ecosystems, such as Llama and Hugging Face, and commercial AI platforms.
Design the architecture of a unified, distributed AI platform spanning complex data processing, model training, inference pipelines, and evaluation frameworks.
Embed with Perception, Prediction, Planning & Control, and Simulation teams 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 across large-scale production environments.
Track developments in AGI and systems engineering and connect external innovations with internal systems to maintain a 1–3-year competitive edge.
Provide technical leadership through architecture design and hands-on coding, debugging, and low-level execution optimization.
Build technical authority, align priorities, and influence engineering teams across algorithms, infrastructure, and hardware to adopt an AI-first engineering paradigm.