AI Transformation Engineer
US$255,000 – US$351,000 / yr base salary
Posted 25 Sep 2026
Advertised as “Staff/Principal AI Transformation Engineer”
BranchFactor Summary
The original posting with the fluff stripped out
- Expected experience
- 8–10 yrs
- Management level
- Individual contributor
- Contract
- Full-time
| Base salary | US$255,000 – US$351,000 / yr |
|---|
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
**
At DiDi Autonomous Driving, we firmly believe that the future of mobility goes beyond simply "utilizing AI"—it will be fundamentally reimagined and entirely driven by an AI-Native architecture.
We are seeking a visionary, highly technical, and mission-driven Staff or Principal AI Transformation Engineer to act as the ultimate catalyst for our company-wide AI transformation. In this strategic, high-impact leadership role, you will combine cutting-edge Large Language Model (LLM) expertise, robust systems architecture design, and a proven track record of enterprise-level AI scaling. You will embed deeply with our core engineering teams to evolve our traditional R&D organization into a truly AI-Native powerhouse.
Key Responsibilities
- AI Infrastructure & Platform Architecture: Spearhead the evaluation, selection, and deep integration of frontier LLM ecosystems (e.g., Llama, Hugging Face) and commercial AI platforms. Own the architectural design of our unified, distributed AI platform spanning complex data processing, model training, inference pipelines, and evaluation frameworks.
- Forward Deployed Execution: Embed directly with core autonomous driving teams (Perception, Prediction, Planning & Control, and Simulation) via the FDE model. Pinpoint engineering bottlenecks, eliminate friction, and translate complex AI capabilities into production-ready internal ecosystems (e.g., AI DevOps, AI Copilots).
- LLMOps / MLOps Orchestration & Optimization: Design and implement highly resilient, scalable automation pipelines for LLM deployment, monitoring, and continuous feedback loops. Optimize GPU cluster utilization, minimize inference latency, and maximize throughput across large-scale production environments.
- Technical Roadmap & Vision: Keep a strong pulse on breakthrough trends in AGI and systems engineering. Act as a "super-connector" between external technological innovations and internal systems, ensuring our AI infrastructure maintains a 1-3 year competitive edge.