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 decision engines for product, growth, fraud, and risk teams.
Domain
Fintech / Intelligent Systems / Customer Intelligence
Deliverable
production ML models
Required skills
Production ML system design, ranking/retrieval, recommendations, search, propensity/churn/LTV modeling, next-best-action decisioning, feature pipelines, model serving, experimentation, monitoring, feedback loops, AI-assisted engineering, trust/fairness/risk evaluation
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
Responsibilities
Design production data and signal contracts defining freshness, provenance, confidence, and eligibility; Own ranking, retrieval, recommendation, and search systems end-to-end; Evaluate customer and business impact including trust, fairness, access, risk, and long-term engagement; Partner across product, growth, data, and risk teams to translate goals into measurable ML designs; Use AI agents to accelerate development and operations.
Seniority
Staff, hands-on IC with strategic scope
