About this role

▸about Coinbase — company background

Ready to do the most impactful work of your career? At Coinbase, we are uncompromising on our mission to increase economic freedom. The bar is high, the environment is intense, and we like it that way. This isn't a place for complacency, it’s a place to be pushed past your perceived limits. If you're ready to build the future of finance alongside people who refuse to settle for "good enough," you belong here. Coinbase is a remote-first, but not remote-only company. Expect to get together quarterly for intense in-person working sessions called “surges.” learn more about working at Coinbase.

As a Senior Machine Learning Engineer on the CX Intelligence Engineering team within Coinbase's Customer Engagement and Experience org, you'll design and build the AI-powered systems that connect our Help Center, chatbots, and agent workflows. This team owns the multi-agent platform powering Coinbase chat, Help Center, and agent tooling surfaces, partnering with Conversation Design, CX, and Engineering teams to deliver secure, compliant, and scalable AI support. You'll drive key CX automation initiatives, including multi-agent framework development, agent assist capabilities, and LLM-powered secure actions that help customers get answers faster while enabling human agents to resolve cases more effectively.

What you'll do:

Architect and deploy the multi-agent framework for agent assist, internal SOP execution, and secure LLM-powered actions, building scalable systems that handle millions of customer interactions.

Own end-to-end project execution for complex ML initiatives, managing priorities, technical trade-offs, and cross-functional dependencies from design through production delivery.

Build and optimize AI-driven concierge chat and agent copilot experiences that improve automated resolution rates and reduce case handling time.

Develop systems to detect and manage bad actors and adversarial inputs, ensuring the multi-agent platform maintains high confidence and safety at scale.

Establish best practices for system design, coding standards, and evaluation frameworks for ML/AI systems, mentoring team members on architectural decisions and modern ML development workflows.