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Staff Applied Machine Learning Engineer- Fraud & Abuse
Remote 10409 Engineering - AIDA role with clear candidate location fit.
PostedRecently added
Eligible countries27 accepted countries
Seniority signalLead
Work settingRemote
Accepted candidate locations
Role overview
Staff Applied Machine Learning Engineer- Fraud & Abuse
Requirements and responsibilities
Readable role content extracted into sections for faster review.
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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