10 hours ago
Base Salary
$255k - $351k/yr
Responsibilities
- Lead evaluation, selection, and integration of frontier LLM ecosystems 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 autonomous driving engineering teams to identify bottlenecks and deliver production-ready internal AI ecosystems such as AI DevOps and AI Copilots.
- Design and implement 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 roadmap for AI infrastructure and connect external AI and systems innovations with internal engineering systems.
- Write core code, debug deep system issues, optimize low-level execution paths, and influence engineering teams toward AI-first development.
Requirements
- Demonstrated ability to design complex system-level architectures while remaining hands-on with core software engineering.
- Proficiency in C++, Python, Java, or JavaScript.
- Experience building technical authority and aligning Algorithms, Infrastructure, and Hardware 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 in distributed LLM training and inference optimization and large-scale compute cluster infrastructure and operations.
- Preferred: familiarity with autonomous driving algorithms, robotics, physics-based simulation engines, or large-scale ML training and serving clusters.
- Preferred: 8–10+ years of professional engineering experience in systems software, core cloud infrastructure, or production-grade machine learning platforms.
- Preferred: experience building AI Copilot applications, autonomous multi-agent frameworks, or developer productivity platforms.
- Preferred: experience delivering projects in complex, high-pressure, or mission-critical environments.
- Preferred: contributions as an open-source maintainer or owner, technical author, blogger, paper author, or industry conference speaker.
Benefits
- Full-time position with bonus, equity, and benefits in addition to base salary.
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.