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# Senior Machine Learning Engineer, Recommendation & Growth

- **Company:** BLBS, LLC
- **Location:** Seattle, WA, United States
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** Java (Programming Language), Artificial Intelligence, Automated Storage and Retrieval Systems, Big Data, C++ (Programming Language), Information Engineering, Software Debugging, MapReduce, Apache Hive, Java Virtual Machine (JVM), Python (Programming Language), Machine Learning, Recommender Systems, Redis, Tensorflow, Software Deployment, Software Requirements Analysis, SQL Databases, Reinforcement Learning, Feature Store, Feature Engineering, Pytorch, Large Language Models, Apache Spark, HNSW (Hierarchical Navigable Small World), Build Management, Apache Flink, Apache Kafka, Milvus, Data Management, Web3.js, Machine Learning Operations, FAISS
- **Published:** October 9, 2026
- **Apply:** https://www.disabledperson.com/jobs/75938968-senior-machine-learning-engineer-recommendation-growth

## About the Role

* 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
 * 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)
 * Ability to collaborate effectively with Asia-Pacific engineering and product teams in Mandarin Chinese.
 * Nice-to-have:
 * Hands-on experience with large-scale data infrastructure: Kafka, Spark/Flink, Redis, feature stores, and online serving systems.
 * Experience in crypto/Web3 or fintech with strong understanding of user behavior in financial contexts; experience with LLM-based personalization or generative recommendation 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; publications at top AI/ML venues (KDD, NeurIPS, WWW, SIGIR, WSDM, CIKM, ICLR, ICML).

## Description

* Design and build low-latency real-time recommendation systems that personalize trading product discovery, 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; campaign targeting logic, subsidy decisions, and customer-facing launch approvals are owned by offshore growth teams.
 * 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 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.