Product Manager (Trust & Safety) – rednote

Job Responsibilities

What You’ll Do

1. Build an AI-native Governance Platform

Participate in the product design of the next-generation AI-native safety and governance platform, connecting the entire governance lifecycle—from risk detection and identification to review, enforcement, and evaluation.

Explore how LLMs and AI agents can be applied to content moderation, investigations, and decision support. Turn human expertise and operational experience into scalable, system-driven capabilities.

2. Build User Trust & Transparency Products

Design user-facing governance experiences, including how moderation outcomes are explained, how enforcement reasons are communicated, and how appeals and reporting processes can be made fairer and more efficient.

Translate complex governance rules into clear, user-friendly product experiences that help users understand and trust the platform, directly contributing to improvements in user trust metrics.

3. Gain Hands-on Experience in Global Markets

Work on governance products for international markets, including non-Chinese-speaking regions. Understand differences in regulatory environments, cultural contexts, and user expectations across markets, and help translate global governance strategies into product solutions.

Collaborate with engineering, algorithms, policy, operations, and data teams to drive the entire product lifecycle, from requirements definition to launch, in a truly global, cross-functional, and cross-time-zone environment.

4. Explore Emerging Risks and New Challenges

Dive into emerging risk areas, including scams, spam accounts, minor safety, and content rating and classification. Help identify new and evolving risk patterns and develop product solutions to address them.

Job Requirements

What We’re Looking For

Curiosity about platform governance and online communities: A genuine interest in platform governance, online content ecosystems, and broader social issues. You are willing to tackle complex problems without predefined answers and find practical, actionable solutions.

Passion for emerging technologies: A strong interest in AI and other cutting-edge technologies. You understand the capabilities and limitations of LLMs and AI agents, and are eager to turn technological advances into practical product capabilities.

User empathy and systems thinking: The ability to balance thoughtful user empathy with structured, systems-level thinking. You can put yourself in the shoes of users who have been penalized while designing mechanisms and workflows that can operate at a scale of hundreds of millions of users.

Structured communication and cross-functional collaboration: Strong communication and stakeholder management skills. You can explain complex rules clearly and align engineering, algorithms, policy, and operations teams to move projects forward.

Data-driven mindset: You use data and experimentation to validate hypotheses and inform decisions rather than relying solely on intuition.

Deep thinking and a growth mindset: You are committed to continuous learning and improvement, and demonstrate courage and resilience when navigating challenges, uncertainty, and ambiguous situations.

Nice to Have

An academic background in Computer Science, Artificial Intelligence, Data Science, Human-Computer Interaction, Law, Sociology, Communications, or a related field.

Internship or project experience in content platforms, community products, risk control, or compliance.

Strong English reading, writing, and communication skills, with the ability to read and understand regulatory documents and platform policy materials in their original language. This role involves working with global markets.

Cross-cultural living or study experience, or first-hand observations of user behavior in international markets.

Regardless of your academic background or whether you have prior hands-on experience in internet product development, we welcome you if you embody these qualities and are ready to put them into practice.

Join us in taking on one of the hardest yet most worthwhile challenges in technology: building a safer, fairer, and more trustworthy online community.

Search & Recommendation 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. Recommendation Algorithms

Drive the implementation and development of recommendation technologies at rednote. Deliver technical breakthroughs in personalized recommendations, content distribution strategies, user understanding, and content understanding.

2. Search Algorithms

Drive the implementation and development of search technologies at rednote. Explore areas including AI-powered search, generative retrieval, new LLM-based relevance paradigms, and large-scale personalized prediction.

3. Multilingual Technologies and Translation

Drive the deployment and application 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.

4. Technical Innovation and Business Exploration

Abstract well-defined technical problems from complex business scenarios and apply cutting-edge technologies, including machine learning and large language models, to improve search and recommendation experiences across content formats such as text-and-image posts, videos, and livestreams. Enhance the experience of millions of users and Inspire Life.

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 large language models, search, recommendation systems, natural language processing (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.

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.