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.

Key Responsibilities

Develop and optimize recommendation models for Bybit’s trading products, content, and community. Leverage large-scale behavioral data to understand user interests and improve recommendation performance and user experience.

Explore cross-domain transfer learning and cold-start strategies tailored to different business scenarios, improving recommendation effectiveness and coverage across domains.

Optimize multi-objective ranking models for CTR, CVR, LTV, and other outcomes. Design approaches to combine ranking objectives and balance clicks, conversions, and long-term value under scenario-specific constraints.

Model cross-scenario user behavior sequences, short- and long-term interests, and changes in intent. Build reusable user representations to address domain transfer, sparse feedback, and cold-start challenges.

Explore generative recommendation approaches, including semantic IDs and generative candidate generation, and integrate them with discriminative recommendation pipelines.

At least four years of experience developing search, advertising, or recommendation algorithms at a consumer-facing internet company, with a deep understanding of candidate retrieval, ranking, and multi-objective modeling.

Strong computer science fundamentals and proficiency in at least one of Python, Java, or C++. Strong software design and coding practices, with a passion for solving challenging technical problems.

Strong analytical and logical thinking skills, with the ability to identify problems and opportunities through data.

A keen interest in emerging technologies and the ability to approach complex problems with clarity and sound reasoning.

Strong business judgment, communication, and collaboration skills, with the ability to translate business needs into effective technical solutions.

Experience in search, advertising, or recommendation algorithms at major technology companies such as ByteDance, Alibaba, Tencent, Meituan, Kuaishou, or Xiaohongshu.

Experience with multi-objective modeling, short- and long-term user sequence modeling, personalized uplift modeling, or generative modeling.

Publications at leading international conferences such as KDD, SIGIR, RecSys, ACL, or NeurIPS.

An AI-driven approach, with hands-on experience bringing LLM or agent applications into production for search, advertising, or recommendation use cases.

A multi-domain recommendation challenge in fintech: Go beyond a single domain such as video or e-commerce. Build personalization capabilities across trading products, market intelligence, community content, campaigns, and AI-powered investment workflows.

Proven business impact with significant room for ML advancement: Our current recommendation system has demonstrated a 71% increase in CTR and a 1,285% increase in conversion rate, with substantial potential for an ML-native upgrade.