AI Algorithm Engineer – LLM/VLM (Mandarin Required)
Experience: 3–5+ Years
Education: Master’s Degree or Above
Employment Type: Full-Time
Sponsorship: H1B Transfers possible
About The Opportunity
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.
Responsibilities
Foundation Models & Multimodal AI
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.
Agentic AI
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.
Foundation Model Training
Participate in the full model lifecycle, including:
Pre-training
Supervised Fine-Tuning (SFT)
Reinforcement Learning (RL)
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.
Applied AI Research
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.
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.
Preferred Experience
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.
Why Join
Work at the intersection of frontier AI research and large-scale consumer products.
Build production AI systems operating across one of the world’s largest content ecosystems.
Engage with cutting-edge LLMs, VLMs, Agentic AI, and multimodal foundation models.
See advances in model capability translate directly into impact for hundreds of millions of users worldwide.