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AML Monitoring Manager
합류하게 될 팀에 대해 알려드려요 토스뱅크 AML Monitoring Team은 Legal & Compliance Division에 소속되어 의심거래보고(STR) 업무를 수행하고 있어요. 토스뱅크 자금세탁방지 조직은 의심거래보고를 담당하는 AML Monitoring Team과 자금세탁방지 기획 및 운영을 담당하는 AML Team으로 나눠져 있지만, 두 팀은 토스뱅크가 자금세탁방지…
Data Analyst (7년 미만)
Data Analyst at Toss Securities analyzes mobile product metrics (LTV, retention, cohorts) using SQL and A/B testing to drive product decisions in a fintech environment.
[코어] Data Analytics Engineer (Feature)
Designs and builds enterprise-wide feature pipelines (ML inputs, search indexes, APIs) for Toss, ensuring data lineage, quality, and lifecycle management while standardizing feature metadata and governance processes.
Data Engineer (Data Service Platform)
Designs and operates scalable data pipelines (ingestion, streaming, batch) for real-time user behavior analytics, enabling targeting, recommendations, and performance tracking in a fintech platform. Core techs: Spark, Kafka, Flink, Airflow, and distributed storage (HBase, Cassandra).
Data Product Manager (AI)
Leads end-to-end AI/ML product development at a fintech bank, defining user-facing features, collaborating with data scientists and engineers, and driving business impact through experiments and metrics.
Data Scientist
Develops and operates machine learning models to optimize loan recovery, pricing, and CRM for a digital bank, using Python, SQL, and MLOps in Seoul.
ML Backend Engineer
Designs and builds backend services for AI-driven banking products, focusing on scalable ML model serving and robust server architecture in a microservices environment.
ML Engineer (커뮤니티 추천)
Build and improve a community-feed recommendation system for a stock-trading app used by 4 million monthly active users, using Python, PyTorch, and cloud-native serving stacks.
ML Engineer [전문연구요원]
ML Engineer at Toss builds and deploys recommendation, search, and AI models across fintech and ecommerce to optimize product exposure, CTR, and user experience using PyTorch, Spark, and Kubernetes.
ML Engineer [Commerce]
Builds and improves machine-learning models that predict click-through and conversion rates to optimize product recommendations for Toss’s commerce platform using PyTorch, TensorFlow, and LightGBM.
ML Engineer (Infra)
Designs and operates ultra-high-performance AI infrastructure (GPU clusters, InfiniBand, Kubernetes) for Toss Securities' ML services, optimizing resource use and eliminating bottlenecks.
ML Engineer (LLM)
Build, fine-tune, and operate LLM/NLP models to simplify complex financial-securities information into user-friendly services at a Korean fintech company.
ML Engineer (ML/LLM Ops)
Build and operate ML/LLM platforms for a Korean neobank, focusing on stable, scalable, and secure model training, deployment, and serving using tools like MLflow, Kubeflow, Triton, and vLLM.
ML Engineer (Platform)
Builds and operates a machine-learning platform for a securities app, focusing on LLM serving, gateway systems, and MLOps tooling in Kubernetes.
ML Engineer (Product)
Builds and deploys AI/ML systems for banking services like fraud detection, lending models, and LLM-based agents in a high-traffic, regulated environment.
Product Manager (LLM)
Leads end-to-end planning, experimentation, and delivery of AI/ML-powered features for a Korean neobank, collaborating with data scientists and engineers to drive user and business impact.
Technical Product Owner (AI)
Owns AI-driven features at Toss, defining how ML models power recommendations, search, and personalization, and measuring their business impact through experiments.
Data Manager (Governance)
합류하게 될 팀에 대해 알려드려요 Data Governance Team 은 Data Platform Tribe 에 소속되어서 토스의 모든 구성원이 데이터를 빠르고, 안전하고, 올바르게 활용할 수 있도록 정책을 정하고 시스템을 만들어서 운영합니다. Data Manager (Governance)는 데이터 직군이에요. 법무나 보안 조직이 규제를 법의 언어로 읽는다면, 이 사람은 같은 규제를…
Product Owner (User Journey)
Owns the AI-driven investment content journey for Toss Securities, from content creation to user discovery and retention, using data and experiments to build trust in AI recommendations.
ML Engineer (Data Pipeline)
Designs, builds, and operates scalable data pipelines for AI/ML products in banking, ensuring reliable batch processing, feature engineering, and model inference workflows using distributed systems like Spark and Airflow.