• Design and maintain end-to-end ML pipelines from data ingestion to model deployment • Operate model registries, feature stores, and experiment tracking (MLflow, W&B) • Build scalable model serving infrastructure on Kubernetes and cloud platforms • Implement CI/CD workflows for ML models, including testing and rollback strategies • Monitor production models — drift detection, alerting, and retraining pipelines • Collaborate with data scientists and platform engineers to ship ML solutions faster

• You will develop AI systems that automate dispute and chargeback handling using structured evidence and business logic, creating a better experience for our customers. • You will build models that automate refunds, getting money back to our customers faster. • You will build and maintain evidence extraction pipelines that process unstructured data using LLM-powered workflows to produce structured, actionable outputs. • You will prototype new modeling ideas, run offline experiments, and drive the best-performing approaches into production with appropriate risk controls. • You will collaborate across Engineering, Servicing Operations, Product, and ML Platform to define requirements, evaluate tradeoffs, and communicate results clearly to both technical and non-technical audiences.

• You will develop AI systems that automate dispute and chargeback handling using structured evidence and business logic, creating a better experience for our customers. • You will build models that automate refunds, getting money back to our customers faster. • You will build and maintain evidence extraction pipelines that process unstructured data using LLM-powered workflows to produce structured, actionable outputs. • You will prototype new modeling ideas, run offline experiments, and drive the best-performing approaches into production with appropriate risk controls. • You will collaborate across Engineering, Servicing Operations, Product, and ML Platform to define requirements, evaluate tradeoffs, and communicate results clearly to both technical and non-technical audiences.

• Research and validate machine learning approaches for DAS-based vessel detection • Train and adapt deep learning and computer vision models for acoustic signal interpretation • Build proof-of-concept AI systems integrating DAS and AIS datasets • Develop scalable ML workflows using Vertex AI and MLFlow • Analyze vessel signatures including engine, propeller, and vibration patterns • Collaborate with cross-functional teams to deliver production-oriented R&D outcomes • Present findings, experiments, and validation results to technical stakeholders.