4 days ago
Base Salary
$255k - $351k/yr
Responsibilities
- Lead evaluation, selection, and integration of frontier LLM ecosystems and commercial AI platforms.
- Architect a unified distributed AI platform for data processing, model training, inference pipelines, and evaluation frameworks.
- Embed with autonomous driving engineering teams to identify bottlenecks and deliver production-ready AI infrastructure, copilots, and internal ecosystems.
- Design resilient LLMOps/MLOps automation for deployment, monitoring, and continuous feedback.
- Optimize GPU cluster utilization, inference latency, and throughput in large-scale production environments.
- Define the technical roadmap for AI infrastructure and connect external AI and systems innovations to internal engineering capabilities.
- Write core code, debug complex systems, and optimize low-level execution paths while influencing multiple engineering organizations.
Requirements
- Proven ability to design complex system-level architectures while remaining hands-on with core software engineering and systems optimization.
- Proficiency in C++, Python, Java, JavaScript, or comparable core programming languages.
- 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.
- Demonstrated ability to influence and align Algorithms, Infrastructure, Hardware, and other engineering teams without formal administrative authority.
- 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 ultra-large-scale ML training and serving clusters is preferred.
- Experience building AI copilot applications, autonomous multi-agent frameworks, or developer productivity platforms is preferred.
- Evidence of technical influence through open-source contributions, technical publications, papers, or industry conference speaking is preferred.
Benefits
- Full-time position with bonus, equity, and benefits in addition to the stated base salary.
- Base salary range is $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.