• Build and operate the ML lifecycle platform. Own the tooling that makes model development reproducible and production-ready, with MLflow (or comparable systems) at the center: experiment tracking, model registry, artifact and metadata management, and versioned, repeatable training and inference pipelines. • Own CI/CD and deployment for ML workloads. Build automated pipelines that move models from notebook to production safely, including packaging, containerization, automated testing and validation, staged rollouts, and rollback. • Make models observable and reliable in production. Stand up monitoring for model and service health, including latency, drift, data-quality, and cost signals, with alerting and clear runbooks so issues surface and resolve quickly. • Build the cloud-native foundations. Contribute to and manage containerized workloads on Kubernetes and codify infrastructure with infrastructure-as-code tooling such as Terraform, keeping environments consistent, secure, and reproducible. • Establish sensible guardrails. Implement infrastructure-level governance for ML systems, including access controls, deployment policies, and auditability, partnering with security and compliance to align with our risk and regulatory requirements. • Enable and mentor the teams you support. Define repeatable patterns and shared services that reduce friction for data and application teams, provide technical guidance and mentorship to junior engineers, and contribute to the direction of dv01's MLOps practices.

• Build and improve the orchestration layer that manages state transitions, context sharing, and intent routing across vendor and internal LLM frameworks in a distributed conversational environment. • Develop production-grade Python services that bridge advanced AI and ML capabilities with reliable customer-facing products. • Drive well-scoped ML projects from design through delivery, balancing technical trade-offs and collaborating across teams. • Contribute to system design, coding standards, and AI/ML development best practices across the team. • Partner with engineers and cross-functional stakeholders to build secure, scalable, and high-performing AI-enabled experiences. • Participate in design reviews and help ensure features meet Coinbase standards for security, compliance, and performance.

• Leverage Machine Learning and advanced data analytics to identify risks and opportunities in our claims management platform. • Analyze and interpret complex data to support the claims management lifecycle. • Contribute directly to the company’s growth and long-term success through actionable insights. • Collaborate with cross-functional teams to transform data into intelligence and intelligence into action.

• Leverage Machine Learning and advanced data analytics to identify risks and opportunities in our claims management platform. • Analyze and interpret complex data to support the claims management lifecycle. • Contribute directly to the company’s growth and long-term success through actionable insights. • Collaborate with cross-functional teams to transform data into intelligence and intelligence into action.