Staff Applied Machine Learning Engineer - Intelligent Data, Signals & Systems
Core
Build production ML systems that transform customer behavior, product context, and feedback loops into trusted signals for recommendations, ranking, risk-aware decisioning, and customer intelligence.
Role type
Staff Applied Machine Learning Engineer (Intelligent Data, Signals & Systems)
Builds
Production ML systems, composable signal interfaces, and AI-assisted workflows for product, growth, fraud, and risk teams.
Domain
Fintech / Intelligent Data & Signals
Deliverable
production ML models
Required skills
Production ML system design and operation, ranking/retrieval/recommendations/search, propensity/churn/LTV modeling, next-best-action decisioning, feature pipeline management, model serving, experimentation design, online/offline consistency, signal interface design, impact evaluation (trust/fairness/risk/compliance), AI-assisted engineering tooling
Preferred skills
Semantic retrieval, embeddings, two-tower models, graph features, LLM-powered retrieval, entity resolution, real-time personalization, multi-objective optimization, long-term holdouts, reusable feature/signal platforms
Technologies
Python, Java, Kotlin, SQL, TensorFlow, PyTorch, XGBoost, LightGBM, Kubernetes, event streams, batch pipelines, feature stores, data warehouses/lakehouses, observability tooling, coding agents
Responsibilities
Build and operate production ML systems turning context into trusted signals; Design production data and signal contracts defining use, freshness, and confidence; Own ranking, retrieval, and recommendation systems end-to-end; Evaluate customer and business impact beyond short-term conversion; Partner across teams to translate goals into measurable ML designs; Use AI agents to accelerate development and operations
Seniority
Staff, hands-on IC with strategic scope
