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Staff Applied Machine Learning Engineer- Fraud & Abuse
Vaga remota de 10409 Engineering - AIDA com fit claro de localização do candidato.
PublicadaAdicionada recentemente
Países elegíveis27 países aceitos
Sinal de senioridadeLead
Modelo de trabalhoRemoto
Locais aceitos para candidatos
Resumo da vaga
Staff Applied Machine Learning Engineer- Fraud & Abuse
Requisitos e responsabilidades
Conteúdo da vaga extraído em seções para revisão mais rápida.
You Will
- Build and operate real-time and batch ML decisioning systems for payment fraud, scams, identity and account integrity, merchant and marketplace risk, and abuse prevention.
- Integrate behavioral, graph, device, network, event-stream, and third-party signals into low-latency model serving, decision APIs, and product controls.
- Own the production lifecycle for risk decisions, including data contracts, feature quality, online/offline consistency, monitoring, drift detection, safe rollout, rollback, and incident response.
- Develop feedback loops and verified AI-assisted workflows for triage, investigation support, alert clustering, graph exploration, simulation, and post-incident learning.
- Partner with modelers, analysts, product, compliance, and operations to balance fraud losses, customer access, false positives, product velocity, support burden, and long-term trust.
- Create reusable decision and evaluation capabilities that product services, internal tools, and AI-assisted workflows can safely consume.
You Have
- 12+ years building and operating production software and ML systems for business-critical products.
- Deep expertise in fraud/risk domains such as payment fraud, identity/account integrity, merchant or marketplace risk, scams, trust & safety, abuse prevention, or compliance decisioning.
- Strong production ML judgment across feature pipelines, model serving, evaluation, monitoring, low-latency integration, safe rollout, and incident response.
- Sound judgment around false-positive tradeoffs, noisy labels, adversarial behavior, customer harm, and cross-functional decisions.
- Experience using AI-assisted engineering tools with appropriate verification, testing, and review for high-stakes systems.
You Have
- Experience with graph-based fraud detection, behavioral sequence models, embeddings, entity resolution, anomaly detection, or human-in-the-loop review.
- Experience building fraud operations tooling for triage, case management, alert clustering, graph exploration, or policy simulation.
- Experience with regulated financial services, model governance, auditability, explainability, or decision logging.
Technologies We Use and Teach
- Python, Java, Kotlin, SQL.
- TensorFlow, PyTorch, XGBoost/LightGBM, embeddings, deep learning, and tree-based modeling ecosystems.
- Kafka or other event-streaming systems, batch data pipelines, feature stores, workflow orchestration, and model-serving systems.
- Cloud infrastructure, Kubernetes, data warehouses/lakehouses, monitoring, observability, coding agents, evaluation harnesses, and agent-assisted operations tooling.
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