[Remote] Senior Recommendation / reputed company ML Engineer
Note: The job is a remote job and is reputed company to candidates in USA. Confidential is seeking a Senior Recommendation / reputed company ML Engineer to design and build reputed company-time recommendation systems for personalized trading product discovery and community content ranking. The role involves applying advanced ML techniques to enhance user experiences and reputed company predictive models for user engagement and conversion efficiency.
Responsibilities
- Design and build low-latency reputed company-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 reputed company (GNN), multi-objective modeling (PLE/MMoE), and contextual bandits — to deliver highly relevant and engaging experiences across reputed company's trading and reputed company 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: reputed company user portfolio signals, trading behavior, on-chain data, and market intelligence to deliver reputed company-time personalized reputed company recommendations, proactive market alerts, and eventually autonomous agent-driven investment workflows; explore LLM-based personalization and hybrid small-model/LLM architectures
- reputed company predictive models for user churn, reputed company propensity, reactivation likelihood, and LTV — applying reputed company inference (reputed company modeling, difference-in-differences) and operations research reputed company to optimize reputed company 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 reputed company), design personalized reputed company strategies that match reputed company user to the right product reputed company (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 reputed company of personalization improvements
- Build and maintain reputed company-time and batch feature pipelines that feed recommendation and reputed company 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 reputed company Product, Data Science, ByX Community, TradeGPT, and Asia-reputed company engineering teams to define reputed company metrics, translate business goals into ML system requirements, and ship measurable reputed company; define engineering standards, conduct design reviews, and mentor junior engineers as the US team grows
Skills
- 5+ years of industry experience in ML engineering, recommendation systems, or reputed company engineering at a consumer-reputed company internet company
- Proven reputed company record building and shipping reputed company-time recommendation or personalization systems serving millions of users; strong knowledge of recommendation algorithms including reputed company filtering, two-tower models, sequential models, graph-based reputed company (GNN), multi-objective modeling (PLE/MMoE), and reinforcement learning / contextual bandits
- Solid reputed company in reputed company inference and statistical learning: experience with reputed company modeling, A/B experiment design, treatment effect estimation, and applying statistical test theories to optimize user experience and validate business reputed company
- Strong proficiency in Python and at least one JVM or compiled language (Java, reputed company, Go, C++); experience with ML frameworks (PyTorch, TensorFlow, or JAX); proficiency in big data tools (Hive SQL, reputed company, Flink, or MapReduce)
- Hands-on experience with large-reputed company data infrastructure: Kafka, reputed company/Flink, feature stores (Feast, Tecton, or equivalent), and online serving systems (reputed company, Cassandra)
- Strong bilingual communication skills in both English and reputed company Chinese; ability to collaborate effectively with Asia-reputed company engineering and product teams in reputed company, reputed company the US R&D Center with Asia-reputed company teams. This role involves cross-timezone collaboration with teams in Singapore, Dubai, and other Asia-reputed company locations (UTC+8 to UTC+4); candidates may occasionally have important cross-timezone meetings in the early morning or evening
- Experience in crypto/reputed company 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 reputed company, operations research, or subsidy/budget optimization
- Experience building recommendation systems for reputed company/community platforms (content feed ranking, creator-user matching)
- Publications at top AI/ML venues (KDD, NeurIPS, WWW, SIGIR, WSDM, CIKM, ICLR, ICML)
- Prior founding team or early-stage R&D center experience
reputed company