About Us

Established in 2018, Bybit is one of the world’s leading cryptocurrency exchanges and digital financial platforms, serving over 80 million users across more than 200 countries and regions. Powered by world-class technology and a user-first mindset, Bybit delivers a seamless ecosystem across trading, payments, wealth management, custody, institutional services, and Web3 — connecting users to the future of digital finance.

Our core values define how we build. We listen, care and improve to create products and experiences that put users first. Backed by a global team of ambitious builders, problem-solvers, and innovators, we foster a high-performance and fast-moving environment where talent is empowered to drive real impact at the global scale. Supported by 24/7 multilingual customer service and a strong commitment to innovation, we are shaping the future of finance through technology, collaboration, and bold execution.

Today, Bybit is recognized as one of the most trusted and transparent platforms in the digital asset industry, continuing to expand its global presence while building the infrastructure for the next generation of financial services.

About the Role

We are at an inflection point where AI is fundamentally reshaping what products can do and how users experience them. We're not just looking for a product manager — we're looking for a hybrid product builder who truly understands AI capabilities and can translate technical potential into real user value.

You will lead end-to-end product design for LLM / Agent-powered applications — from zero to one — across domains including intelligent customer service, personalized recommendations, AI assistants, and automated workflows. You are the critical connector between user insights, AI technical capabilities, and business objectives: someone who can discuss RAG retrieval strategies and multi-agent orchestration with ML engineers, while clearly articulating product value and delivery roadmaps to business stakeholders.

If you have genuine passion for AI-native products and the ability to define clear product direction amid ambiguity and rapid change, we'd love to build the next generation of AI applications together.

Key Responsibilities

End-to-End AI Product Lifecycle ManagementOwn the full product lifecycle of AI applications — including intelligent chatbots, AI assistants, recommendation systems, and automated workflows — from requirements definition and product design through to launch and delivery. Produce high-quality PRDs, interaction flow diagrams, and acceptance criteria while driving cross-functional teams to execute efficiently.

LLM / Agent Capability ProductizationDeeply engage in Agent product design, including task planning workflows, tool-calling strategies (Function Calling / MCP), context management mechanisms, multi-agent orchestration logic, and graceful failure/fallback handling. Translate underlying model capabilities into meaningful, perceivable user value.

AI Evaluation Framework & Quality StandardsBuild Harness evaluation frameworks for AI products — designing test scenario sets, evaluation metric systems (accuracy, hallucination rate, task completion rate, etc.), and regression test case libraries. Independently assess AI capability boundaries and drive continuous model iteration and product quality improvement.

Conversational Product Design & Knowledge EngineeringLead platform architecture planning for intelligent customer service and conversational assistant products, covering knowledge base structure design, dialogue flow and SOP design, corpus annotation and training strategy, intelligent reply generation, and case summarization. Leverage large-scale data analysis to continuously optimize conversation quality and issue resolution rates.

Personalization Engines & Generative UI ProductsCollaborate deeply with ML and data teams to design recommendation strategies that deliver truly personalized, individualized user experiences. Define generative UI product requirements and drive growth in core business metrics including CTR, conversion rate, and user retention. Establish comprehensive attribution analysis frameworks for recommendation effectiveness.