18 hours ago
Base Salary
$255k - $351k/yr
Responsibilities
- Lead evaluation, selection, and integration of LLM ecosystems and commercial AI platforms.
- Architect a unified distributed AI platform spanning data processing, model training, inference pipelines, and evaluation frameworks.
- Embed with autonomous driving engineering teams to identify bottlenecks and deliver production-ready internal AI ecosystems, including AI DevOps and AI Copilots.
- Design resilient automation pipelines for LLM deployment, monitoring, and continuous feedback.
- Optimize GPU cluster utilization, inference latency, throughput, and large-scale production infrastructure.
- Define the technical vision and roadmap for AI infrastructure and connect external innovations with internal systems.
- Influence Algorithms, Infrastructure, Hardware, and other engineering teams toward an AI-first engineering model without relying on formal authority.
Requirements
- Proven ability to design complex system architectures while writing core code, debugging deep system issues, and optimizing low-level execution paths.
- Proficiency in one or more core languages including C++, Python, Java, or JavaScript.
- Experience leading or substantially contributing to an enterprise-scale AI-native transformation or building an enterprise-grade AI/ML platform from 0 to 1.
- Hands-on experience deploying, tuning, and optimizing PyTorch, Ray, vLLM, Triton Inference Server, Kubernetes, DeepSpeed, and Megatron-LM or comparable tools.
- Deep practical experience with distributed LLM training and inference optimization and large-scale compute cluster infrastructure and operations.
- Demonstrated ability to align priorities and drive diverse engineering teams toward adoption of new technical paradigms.
- Preferred: familiarity with autonomous driving algorithms, robotics, physics-based simulation engines, or ultra-large-scale ML training and serving clusters.
- Preferred: 8–10+ years of professional engineering experience in systems software, core cloud infrastructure, or production machine learning platforms.
- Preferred: experience building AI Copilot applications, autonomous multi-agent frameworks, or developer productivity platforms.
- Preferred: experience steering delivery in complex, high-pressure, or mission-critical environments.
- Preferred: contributions to the broader technology community through open source, technical writing, papers, or conference speaking.
Benefits
- Full-time position with bonus, equity, and benefits in addition to base salary.
- Annual base salary range of $255,000–$351,000, with pay determined by location, skills, experience, and education or training.
- The posting includes a California Residents Privacy Notice.
Tech Stack
Categories
About DiDi
DiDi is a mobility technology platform offering app-based ride hailing, taxi hailing, shared mobility, food delivery, and intra-city freight to consumers and businesses across Asia Pacific, Latin America, and other markets. It connects riders with drivers and couriers and provides vehicle, energy, and insurance-related services, earning transaction commissions and service fees. DiDi launched an autonomous driving unit in 2016, which became the independent DiDi Autonomous Driving in 2019 to develop Level 4 technology for its network.