Staff/Principal Forward Deployed Engineer at DiDi Labs

San Jose, California, United States -

Full Time

Start Date

Immediate

Expiry Date

09 Oct, 26

Salary

351000.0

Posted On

11 Jul, 26

Experience

10 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

LLM, Systems Architecture, C++, Python, PyTorch, Kubernetes, MLOps, Distributed Training, GPU Optimization, Ray, vLLM, Triton Inference Server, DeepSpeed, Megatron-LM, AI Infrastructure, Autonomous Driving

Industry

IT Services and IT Consulting

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 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 / Principal Forward Deployed Engineer (FDE) 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. Qualifications & Experience Architectural Vision + Hands-on Execution: A proven technical leader who can design complex, system-level architectures while maintaining a fierce passion for writing core code, debugging deep system issues, and optimizing low-level execution paths. Proficiency in core languages such as C++, Python, Java, JavaScript, etc. Cross-Functional Influence: Demonstrated ability to build technical authority, align priorities, and drive diverse engineering teams (Algorithms, Infrastructure, Hardware) toward adopting an AI-first engineering paradigm without relying on formal administrative authority. Enterprise AI Transformation: Proven experience leading or heavily contributing to a large-scale corporate "AI-native transformation," or a track record of building enterprise-grade AI/ML platforms from 0 to 1. Deep AI Tech Stack Expertise: Thorough hands-on deployment, tuning, and optimization experience with mainstream AI infrastructure tools and frameworks, including but not limited to PyTorch, Ray, vLLM, Triton Inference Server, Kubernetes, DeepSpeed, and Megatron-LM. Hardcore AI Infra Experience: Years of deep, practical experience in distributed LLM training/inference optimization and large-scale compute cluster infrastructure & operations (I&O). Preferred Qualifications Domain Expertise: Familiarity with autonomous driving algorithms (Perception, Planning, Control, Simulation), robotics, physics-based simulation engines, or ultra-large-scale ML training/serving clusters is highly preferred. Senior Industry Track Record: 8-10+ years of professional engineering depth in systems software, core cloud infrastructure, or production-grade machine learning platforms. Agentic Frameworks & Developer Productivity: Hands-on experience building custom AI Copilot applications, autonomous Multi-Agent Frameworks, or high-tier developer productivity platforms. Thriving in Complexity: Proven success steering core project delivery amidst complex business logic, fast-paced/high-pressure environments, or mission-critical systems. Technical Influence: An active contributor to the broader tech community (e.g., open-source maintainer/owner, author of high-quality technical blogs/papers, or speaker at premier industry AI/ML conferences). The base salary range for this full-time position is $255,000 -$351,000 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

Responsibilities

Lead the company-wide AI transformation by designing and integrating a unified, distributed AI platform for autonomous driving. Embed with core engineering teams to translate complex AI capabilities into production-ready internal ecosystems and optimize LLM deployment pipelines.