Senior Backend Software Engineer, Coinbase

• 5+ years of experience in software engineering, with proven expertise in designing, building, and maintaining production services using a microservices architecture
• Hands-on experience building LLM-based applications or AI/ML systems in production, including prompt engineering, retrieval-augmented generation (RAG), or agentic AI frameworks
• Proficiency in Python and Golang (or comparable backend languages), with experience integrating third-party AI models and APIs into production workflows
• Track record of delivering end-to-end projects with measurable business impact in ambiguous problem spaces
• Experience building data pipelines, integrating with external vendors, or working with large-scale unstructured data sources (e.g., blockchain data, customer interaction logs)
• Utilizes generative AI responsibly, maintaining human oversight to deliver business-ready outputs and drive measurable improvements in workflow efficiency, cost, and quality

Desirable

• As a Senior Software Engineer on the Platform Core Automation team, you'll build the AI infrastructure and agentic systems that automate customer support and compliance operations at Coinbase
• This team is reimagining what these processes look like when AI handles them end-to-end, replacing manual workflows with intelligent agents that resolve customer issues and execute compliance tasks autonomously
• You'll own the design and delivery of LLM-powered systems, grounding techniques, and integration pipelines that directly reduce resolution times, cut costs, and improve accuracy across millions of customer interactions
• Own the design and delivery of agentic AI systems that power Coinbase's customer support and compliance automation, from LLM orchestration through production deployment and monitoring
• Build scalable, secure backend infrastructure in Python and Golang that serves AI workloads, including model integration pipelines, guardrails, grounding mechanisms, and measurement frameworks
• Drive end-to-end project execution on complex AI initiatives, making technical trade-offs across latency, accuracy, cost, and reliability
• Partner with Customer Experience, Compliance, and product engineering teams to identify high-impact automation opportunities and translate operational pain points into AI-powered solutions
• Strengthen engineering quality across the team by leading code reviews, mentoring engineers on AI system design patterns, and contributing to on-call rotations and incident response