Base Salary
$255k - $351k/yr
Responsibilities
- Lead evaluation, selection, and integration of frontier LLM ecosystems such as Llama and Hugging Face and commercial AI platforms.
- Own the architecture of a unified distributed AI platform covering data processing, model training, inference pipelines, and evaluation frameworks.
- Embed with Perception, Prediction, Planning & Control, and Simulation teams to identify engineering bottlenecks and deliver production-ready internal AI ecosystems.
- Design and implement resilient, scalable LLMOps/MLOps automation pipelines for deployment, monitoring, and continuous feedback.
- Optimize GPU cluster utilization, inference latency, and throughput across large-scale production environments.
- Define the technical roadmap for AI infrastructure and connect external AI and systems innovations to internal engineering systems.
Requirements
- Proven ability to design complex system-level architectures while writing core code, debugging deep system issues, and optimizing low-level execution paths.
- Proficiency in core languages such as C++, Python, Java, and JavaScript.
- Experience influencing diverse Algorithms, Infrastructure, and Hardware engineering teams without relying on formal administrative authority.
- 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 PyTorch, Ray, vLLM, Triton Inference Server, Kubernetes, DeepSpeed, and Megatron-LM.
- Deep practical experience with distributed LLM training and inference optimization and large-scale compute cluster infrastructure and operations.
- 8–10+ years of professional engineering experience in systems software, core cloud infrastructure, or production-grade machine learning platforms.
- Familiarity with autonomous driving algorithms, robotics, physics-based simulation engines, or very large-scale ML training and serving clusters is preferred.
- Hands-on experience building AI Copilot applications, autonomous multi-agent frameworks, or developer productivity platforms is preferred.
- Proven success delivering core projects in complex, fast-paced, high-pressure, or mission-critical environments.
- Technical community contribution through open-source maintenance, technical publications, papers, or industry AI/ML conference speaking is preferred.
Benefits
- Full-time position with bonus, equity, and benefits.
Tech Stack
Categories
About DiDi
DiDi Global Inc. is a leading mobility technology platform. It offers a wide range of app-based services across Asia Pacific, Latin America, and other global markets, including ride hailing, taxi hailing, designated driving, hitch and other forms of shared mobility as well as certain energy and vehicle services, food delivery, and intra-city freight services. DiDi provides car owners, drivers, and delivery partners with flexible work and income opportunities. It is committed to collaborating with policymakers, the taxi industry, the automobile industry, and the communities to solve the world’s transportation, environmental, and employment challenges through the use of AI technology and localized smart transportation innovations. DiDi strives to create better life experiences and greater social value, by building a safe, inclusive, and sustainable transportation and local services ecosystem for cities of the future.