Agent / LLM Algorithm Engineer – rednote

About the Team

The Global Search & Recommendation Algorithm Team is a core force behind rednote’s global expansion strategy. Our work spans key areas including community recommendation, search, AI-powered search, translation, and multilingual foundation models. In early 2025, our LLM-powered translation technology gained widespread attention, showcasing the team’s technical capabilities and impact.

Global expansion calls for a fundamental rethinking of how we build products. Unlike mature businesses focused on continuous optimization, rednote offers us a genuine 0-to-1 opportunity: fewer legacy constraints, greater potential for innovation, and the chance to reshape search and recommendation algorithms through an AI-native approach.

Here, you’ll tackle some of the most challenging algorithmic problems in real-world global business scenarios, including multilingual understanding, cross-cultural relevance, cold-start challenges, and cross-domain understanding.

If you’re eager to explore uncharted territory and solve complex technical challenges, join us in building the next generation of community products for users around the world.

Job Responsibilities

1. Multilingual and Multimodal LLM Post-training

Drive the post-training of multilingual and multimodal foundation models, as well as the deployment of multilingual foundation models and translation technologies. Enable cross-lingual understanding across multiple modalities, including text, images, audio, and video, helping users who speak different languages communicate seamlessly.

2. AI Agents for Recommendation Algorithms

Drive the application of AI agent technologies to recommendation systems at rednote. Explore innovative approaches including user memory, generative retrieval, and new agent-based paradigms for improving recommendation performance.

3. AI Agents for Search Algorithms

Drive the application of AI agent technologies to search systems at rednote. Explore areas including AI-powered search, generative retrieval, and new LLM-based relevance paradigms.

Job Requirements

Educational Background: Bachelor’s degree or above in Computer Science, Artificial Intelligence, Electronic Information Engineering, Automation, Software Engineering, Mathematics, or a related field.

Strong Machine Learning Fundamentals: A solid foundation in machine learning. Candidates who have published high-quality research as first authors at leading international conferences such as RecSys, KDD, NeurIPS, ICML, ACL, SIGIR, or ICLR are preferred. Top placements in well-known competitions such as KDD Cup, Kaggle, or ICPC are also a plus.

Relevant Research or Internship Experience: Research or internship experience in LLM, search/recommendation systems, NLP, or multilingual technologies is preferred.

Strong Programming Skills: Solid programming fundamentals and familiarity with common data structures and algorithms. Proficiency in at least one of Java, C++, or Python.

English Communication Skills: Good English communication skills and the ability to work effectively in cross-cultural environments are preferred.

Ownership and Analytical Thinking: A proactive, self-driven mindset, strong logical reasoning, and the ability to think abstractly.

What You’ll Gain

Build a global product from 0 to 1: Play an integral role in building the algorithmic systems of a global product from the ground up, with the opportunity to help define its technical direction and development path.

Tackle large-scale challenges and shape the future of AI-native search and recommendation: Work with large-scale datasets and cutting-edge technologies in real-world multilingual scenarios, collaborating with the team to establish new paradigms for AI-native search and recommendation.

Stay at the forefront of research: Receive support for publishing research at top-tier academic conferences and maintain close connections with the research community.