Multimodal LLM & Agent Algorithms Engineer

rednote; Singapore; On-site; SGD 120,000 - 180,000; Full time

Job summary

rednote is expanding its International Trust & Safety Algorithm Team to build a global AI-native content governance and understanding system. You will drive end-to-end R&D for AI Agents, LLM/VLM training, and multimodal content understanding, collaborating across cross-functional teams to maintain technical leadership.

A strong track record in research and publications is a plus. This role focuses on state-of-the-art agent architectures, safety, and governance for a global community, with

Qualifications

• Bachelor's degree or above in Computer Science, Artificial Intelligence, Machine Learning, Big Data, Mathematics, or a related field.
• In-depth understanding of large models and AI Agents; familiarity with LLM paradigms, prompt engineering (PE), retrieval-augmented generation (RAG), and evaluation frameworks; knowledge of Multi-Agent architectures (Multi-Agent, Context Engineering/Management, ReAct/PlanAct/CodeAct).
• Familiar with Agentic RL and related infrastructure; ability to apply SFT and RL to optimize models for specific problems.
• Research-oriented mindset; publications at top conferences are a strong plus.

Responsibilities

• Build on AI capabilities such as VLM/Agent to fuse multi-dimensional signals—text, images, video, and audio across content types and scenarios including notes, comments, accounts, transactions, and livestreaming.
• Establish and evolve Rednote's content feature and grading systems, feeding core pipelines in governance, search, and traffic control.
• Own end-to-end R&D of Agent systems for governance, content understanding, and editing, covering ReAct/Tool Use/Multi-Agent architectures and related optimization.
• Advance full-pipeline training of LLM/VLM, including pre-training, SFT, and RL, to build foundational multimodal AI capabilities.
• Continuously track advancements in the AI Agent domain and promote cutting-edge techniques, publishing papers at top conferences.

Skills

LLMs & AI Agents

Multi-Agent Systems

Prompt Engineering

ReAct/PlanAct/CodeAct

MCP/A2A/Function Calling

Education

Bachelor’s degree or above in CS/AI/ML/Big Data/Math

Tools

CodeRL

Reward Systems

SFT/RL Training

Job description

rednote is expanding its International Trust & Safety Algorithm Team to build a global AI-native content governance and understanding system. You will drive end-to-end R&D for AI Agents, LLM/VLM training, and multimodal content understanding, collaborating across cross-functional teams to maintain technical leadership.

A strong track record in research and publications is a plus. This role focuses on state-of-the-art agent architectures, safety, and governance for a global community, with