Hiring: Senior Machine Learning Engineer, Recommendation & Growth Company: ByLabs Location: Seattle, WA Job Posted Time: 2026-09-10 13:39:11 Target Skills & Keywords : C++, Feature Store, Flink, Hive, Java, Kafka, LLM, MapReduce, Milvus, PyTorch, Python, Redis, Reinforcement Learning, SQL, Scala, Spark, TensorFlow, Web3 About the job Experience: •5+ years of industry experience in ML engineering, recommendation systems, or growth engineering at a consumer-scale internet company Required Skills: •Build the ML infrastructure and model research layer for AI-powered personalization for real-time ranking and retrieval systems. •Build the recommendation and experimentation infrastructure for user lifecycle management; US persons are excluded from any targeting universe. •Develop predictive models for user churn, upgrade propensity, reactivation likelihood, and LTV — applying causal inference (uplift modeling, difference-in-differences) and operations research methods. •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, 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. Qualifications: •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 •Build high-throughput real-time feature pipelines (Kafka/Flink) enabling minute-level user behavioral feature updates; contribute to a unified online/offline Feature Store architecture, governing feature consistency and eliminating time-travel leakage across training and serving. •Own the construction and optimization of large-scale vector retrieval systems (Faiss/Milvus/HNSW) supporting candidate pools scaling from thousands to millions of heterogeneous items (trading products, news, KOL content, on-chain signals). •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) •Demonstrated capacity to collaborate effectively with Asia-Pacific engineering and product teams in Mandarin Chinese. •Applied hands-on capability in large-scale data infrastructure: Kafka, Spark/Flink, Redis, feature stores, and online serving systems. Interested candidates, please apply directly through the job posting on company's career page or try via AI auto apply on this platform. Don't miss this opportunity to join a forward-thinking team!