2 days ago
Base Salary
$255k - $351k/yr
Responsibilities
- Lead evaluation, selection, and integration of frontier LLM ecosystems and commercial AI platforms.
- Architect and own 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 LLMOps/MLOps automation for deployment, monitoring, and continuous feedback.
- Optimize GPU cluster utilization, inference latency, and throughput across large-scale production environments.
- Set the technical AI transformation roadmap and connect external innovations with internal systems.
- Write core code, debug deep system issues, and optimize low-level execution paths.
- Influence Algorithms, Infrastructure, and Hardware teams toward an AI-first engineering paradigm without relying on formal authority.
Requirements
- Demonstrated ability to design complex system-level architectures while maintaining hands-on coding, debugging, and low-level optimization responsibilities.
- Proficiency in core languages such as C++, Python, Java, and JavaScript.
- Proven experience leading or heavily 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.
- Ability to build technical authority, align priorities, and influence diverse engineering teams.
- Preferred: 8-10+ years of professional engineering experience in systems software, core cloud infrastructure, or production-grade machine learning platforms.
- Preferred: familiarity with autonomous driving algorithms, robotics, physics-based simulation engines, or ultra-large-scale ML training and serving clusters.
- Preferred: experience building AI Copilot applications, autonomous multi-agent frameworks, or developer productivity platforms.
- Preferred: success delivering projects in complex, high-pressure, or mission-critical environments.
- Preferred: open-source maintenance, technical authorship, or speaking at major AI/ML conferences.
Benefits
- Full-time position with bonus, equity, and benefits in addition to base salary.
- Base salary range of $255,000-$351,000 annually, with pay determined by location, skills, experience, and education or training.
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.