• Deployment and integration of computer vision solutions for next-generation satellite imagery analytics. • Ensure high-quality and production-ready Python or Go code. • Manage cloud infrastructure and code deployment to AWS, Kubernetes, or other environments. • Take part in the entire project lifecycle from requirements development to deployment. • Serve as a lead on MLOps software development projects, data ETL projects, and software feature development. • Collaborate with management and technical team on technical roadmap and implementation strategy.

Role Description As a Staff Applied Machine Learning Engineer focused on Fraud & Abuse, you will design, build, and operate production ML decision systems that reduce payment fraud, account takeover, identity abuse, merchant and marketplace risk, scams, and other adversarial activity across Block. The team optimizes for reliable decisions, safe deployment, and measurable customer outcomes — preserving access for good customers while reducing fraudulent, abusive, or unsafe activity. You should be comfortable owning production systems end to end: - Data contracts - Low-latency inference - Batch scoring - Feature quality - Online/offline consistency - Model deployment - Monitoring - Incident response - Rollback - Outcome feedback loops The work combines large-scale ML decisioning with AI-assisted operations: - Surfacing evidence - Simulating controls - Accelerating triage - Improving feedback loops while preserving human judgment in high-stakes decisions You will work closely with ML modelers, product engineers, risk analysts, compliance partners, and operations teams to respond quickly to evolving abuse patterns without creating unnecessary friction or harm for legitimate customers. Qualifications - 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 Requirements - 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 Benefits - Remote work - Medical insurance - Flexible time off - Retirement savings plans - Modern family planning

• Build and deploy models to detect and flag suspicious behavior (classification, anomaly detection, clustering, ranking, and other approaches as appropriate) • Own ML pipelines end-to-end (feature generation, training, evaluation, batch/streaming inference, backfills, and versioning) • Design evaluation + monitoring: ground truth strategies with Threat Intel, offline metrics, drift monitoring, and alerting • Collaborate with product & engineering to integrate intelligence into user-facing workflows (e.g., review queues, and decision-support tooling) • Improve data quality and accessibility by working with data engineering patterns (schemas, lineage, reproducibility, and access controls) • Contribute to operational rigor: runbooks, incident response, and safe rollout practices for systems that impact customer trust

• Deliver end-to-end ML use cases (recommendation systems, safety algorithms, moderation models, etc.). • Design, train, evaluate, and deploy ML models where appropriate. • Balance speed of delivery with robustness and reliability. • Drive improvements across the ML lifecycle (training, deployment, monitoring, iteration). • Establish KPIs and monitoring standards to track model performance over time. • Ensure continuous alignment with product and safety objectives. • Help improve the reliability and observability of production ML systems. • Contribute to the evolution of our ML platform through tools, workflows, and reusable components. • Help establish and promote scalable ML engineering practices across teams. • Take ownership of legacy models and realign them with current business needs. • Improve, retrain, and integrate them into modern pipelines. • Explore and implement advanced ML approaches where relevant. • Partner with Data Engineering, MLOps, Backend Platform, and Product teams. • Act as a bridge between ML, platform, and business stakeholders.