Machine Learning Engineer

Coinbase is 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.”

• Architect and deploy the orchestration layer that manages state transitions, context sharing, and intent routing across vendor and internal LLM frameworks in a distributed conversational environment.
• Build production-grade Python services that bridge advanced ML/AI research with reliable, measurable customer-facing products.
• Lead end-to-end project execution for complex ML initiatives, managing priorities, technical trade-offs, and cross-functional dependencies from design through delivery.
• Establish best practices for system design, coding standards, and AI/ML development workflows across the team.
• Mentor engineers on architectural integrity and modern AI/ML patterns, raising the technical bar for the broader team.
• Conduct design reviews to ensure every feature meets Coinbase's standards for security, scalability, and performance.

• 5+ years of professional experience in machine learning and software engineering, with a track record of shipping production-grade ML services at scale.
• Hands-on expertise building with modern AI architectures (LLMs, deep learning) and the generative AI ecosystem, including frameworks such as LangGraph, LangSmith, Google ADK, Vertex AI, or AWS Bedrock.
• Deep proficiency in Python with demonstrated ability to write clean, maintainable, highly-tested production code.
• Specialized knowledge in at least one domain: NLP, information retrieval, computer vision, or advanced statistical modeling.
• Proven ability to write technical design documents and present ML system architectures to cross-functional stakeholders, translating complex technical concepts for non-technical audiences.
• Utilizes generative AI responsibly, maintaining human oversight to deliver business-ready outputs and drive measurable improvements in workflow efficiency, cost, and quality.