AI Algo Eng - LLM/VLM Mandarin Required

Fuku•San Francisco, CA

About The Position

Our client is a global leader in consumer internet platforms, serving hundreds of millions of users worldwide across content, community, and commerce. As the platform expands internationally, frontier AI is central to understanding, moderating, and improving large-scale multimodal content. We are seeking experienced AI Algorithm Engineers to develop the next generation of production AI systems, including Large Language Models (LLMs), Vision Language Models (VLMs), Agentic AI, and multimodal foundation models. This role involves working at the intersection of AI research and production engineering, directly influencing content understanding, recommendation quality, content governance, and intelligent automation at a global scale. This is not an LLM application or wrapper role.

Requirements

Master’s degree or above in Computer Science, Artificial Intelligence, Machine Learning, Mathematics, or related disciplines.
Approximately 3–5+ years of relevant industry experience.
Strong understanding of Large Language Models, Vision Language Models, Multimodal Foundation Models, Agentic AI.
Hands-on experience with Prompt Engineering, Retrieval-Augmented Generation (RAG), Agent evaluation frameworks, Model evaluation.
Experience with modern Agent architectures, including ReAct, PlanAct, CodeAct, Multi-Agent Systems, Context Engineering, Function Calling, MCP, A2A.
Familiarity with SFT, RLHF/RL, Post-Training, Reward Models, CodeRL or equivalent reinforcement learning infrastructure.
Strong engineering skills with the ability to translate research ideas into production AI systems.
Professional Mandarin communication skills are required due to close collaboration with engineering and research teams based in China.

Nice To Haves

Production experience with Vision Language Models or multimodal foundation models.
Experience building production Agent systems at significant scale.
Background in recommendation systems, search, content understanding, trust & safety, or content moderation.
Experience with large-scale model training or post-training.
Publications at conferences such as NeurIPS, ICLR, ICML, CVPR, ICCV, ACL, EMNLP, or KDD are advantageous.

Responsibilities

Develop production AI systems based on LLMs, VLMs, and multimodal foundation models.
Design algorithms to understand complex signals across text, images, video, and audio.
Build scalable multimodal intelligence for large-scale content understanding and decision-making.
Enhance semantic understanding, classification, retrieval, and reasoning across diverse content formats.
Design and build production-grade Agent systems, including ReAct, PlanAct/CodeAct, Tool Use & Function Calling, Multi-Agent Architectures, Context Engineering & Memory Management, MCP/A2A, Agent Orchestration, Task Planning & Intent Understanding.
Productionise Agent systems to deliver measurable improvements to real-world products used by hundreds of millions of users.
Participate in the full model lifecycle, including Pre-training, Supervised Fine-Tuning (SFT), Reinforcement Learning (RL), and Post-Training.
Improve model reasoning, planning, and multimodal capabilities.
Design evaluation methodologies for models and Agent systems.
Develop reward models and optimization pipelines for real-world production objectives.
Evaluate emerging developments in LLMs, VLMs, Agentic AI, and multimodal learning.
Translate frontier research into scalable production systems.
Contribute to technical direction, architecture, and AI best practices.
Opportunities to contribute to publications at leading AI conferences.