Senior Recommendation / Growth ML Engineer

Key Responsibilities

• Design and build low-latency real-time recommendation systems that personalize trading product discovery, campaign targeting, content feeds, and community content ranking (ByX) for users across web and mobile surfaces — covering the full ML lifecycle from data preparation and feature engineering to model training, evaluation, and production deployment
• Apply advanced ML personalization techniques — including two-tower retrieval, sequential models, graph-based methods (GNN), multi-objective modeling (PLE/MMoE), and contextual bandits — to deliver highly relevant and engaging experiences across Bybit's trading and social surfaces; explore multi-scenario joint modeling to unify signals across trading, community, and campaign surfaces
• Build the personalization layer for TradeGPT's AI investment assistant: integrate user portfolio signals, trading behavior, on-chain data, and market intelligence to deliver real-time personalized token recommendations, proactive market alerts, and eventually autonomous agent-driven investment workflows; explore LLM-based personalization and hybrid small-model/LLM architectures
• Develop predictive models for user churn, upgrade propensity, reactivation likelihood, and LTV — applying causal inference (uplift modeling, difference-in-differences) and operations research methods to optimize intervention timing, channel selection, and subsidy allocation for maximum conversion efficiency
• Build a full user lifecycle data and value system: establish behavioral signals that predict key conversion milestones (first deposit, first trade, VIP upgrade), design personalized intervention strategies that match each user to the right product hook (CopyTrading, TradeGPT, Earn, task rewards) based on their risk profile and behavioral history, and measure treatment effects rigorously
• Architect and own the full-stack A/B experimentation infrastructure, including experiment assignment, metric pipelines, statistical analysis frameworks (CUPED, sequential testing), and self-serve tooling for product teams; design and execute conversion lift studies to validate the business impact of personalization improvements
• Build and maintain real-time and batch feature pipelines that feed recommendation and growth models; partner with data engineering on feature store design; ensure end-to-end system observability and debugging tooling for production recommendation services
• Partner closely with Growth Product, Data Science, ByX Community, TradeGPT, and Asia-Pacific engineering teams to define success metrics, translate business goals into ML system requirements, and ship measurable impact; define engineering standards, conduct design reviews, and mentor junior engineers as the US team grows

Major Requirements

• 5+ years of industry experience in ML engineering, recommendation systems, or growth engineering at a consumer-scale internet company
• Proven track record building and shipping real-time recommendation or personalization systems serving millions of users; strong knowledge of recommendation algorithms including collaborative filtering, two-tower models, sequential models, graph-based methods (GNN), multi-objective modeling (PLE/MMoE), and reinforcement learning / contextual bandits
• Solid foundation in causal inference and statistical learning: experience with uplift modeling, A/B experiment design, treatment effect estimation, and applying statistical test theories to optimize user experience and validate business decisions
• Strong proficiency in Python and at least one JVM or compiled language (Java, Scala, Go, C++); experience with ML frameworks (PyTorch, TensorFlow, or JAX); proficiency in big data tools (Hive SQL, Spark, Flink, or MapReduce)
• Hands-on experience with large-scale data infrastructure: Kafka, Spark/Flink, feature stores (Feast, Tecton, or equivalent), and online serving systems (Redis, Cassandra)
• Strong bilingual communication skills in both English and Mandarin Chinese; ability to collaborate effectively with Asia-Pacific engineering and product teams in Mandarin, bridging the US R&D Center with Asia-Pacific teams. This role involves cross-timezone collaboration with teams in Singapore, Dubai, and other Asia-Pacific locations (UTC+8 to UTC+4); candidates may occasionally have important cross-timezone meetings in the early morning or evening
• Nice-to-have: experience in crypto/Web3 or fintech with strong understanding of user behavior in financial contexts; experience with LLM-based personalization or hybrid small-model/LLM architectures; experience in multi-scenario joint modeling (unifying signals across search, recommendation, and marketing); experience with LTV prediction, operations research, or subsidy/budget optimization; experience building recommendation systems for social/community platforms (content feed ranking, creator-user matching); publications at top AI/ML venues (KDD, NeurIPS, SIGIR, WSDM, CIKM, ICLR, ICML); prior founding team or early-stage R&D center experience