• Work with Imogen to modernize production mainframe systems in collaboration with customers and partners • Work as part of small, cross-functional XP teams, pairing daily and collaborating closely with product managers, designers, client engineers, partners, and internal platform teams • Lead and facilitate technical discovery sessions and workshops, including on-site with customers • Apply XP engineering practices such as pair programming, TDD, refactoring, and CI/CD • Use and experiment with LLM-assisted coding tools • Design and operate production systems with a focus on durability, operability, and eventual client and partner ownership • Execute delivery work in a way that enables partner engineers to independently deliver modernization efforts • Collaborate closely with partner engineers through pairing, mentoring, and shared problem solving • Work flexibly across time zones when collaborating with partners

• The Full-stack Software Engineer will build the work needed to implement functionality from the product backlog. • Tests deliverables against a user story’s acceptance tests and unit tests; • The Full-stack Software Engineer will execute necessary documentation as directed or needed. • Works closely with other team members to ensure that features meet business. • Analyze and decompose complex user stories and epics into manageable tasks. • Collaborate with project managers and team members to plan and estimate sprint tasks. • Offer expertise in architectural and design decisions, as a subject matter expert in SQL, C#, .NET, REST, JSON, and Angular(or React) technologies. • Guide and mentor junior developers, fostering their growth and understanding of industry best practices. • Craft high-quality, scalable, and secure code. • Conduct thorough code reviews (PRs) to uphold code quality and adherence to design principles. • Identify, troubleshoot, and enhance existing software solutions. • Collaborate closely with engineers, product managers, and stakeholders to maintain clear communication and streamline workflows. • Communicate technical concepts effectively to non-technical team members. • Facilitate team code reviews, training, and planning sessions. • Operate effectively in high-stress environments, managing multiple priorities concurrently. • Quickly adapt to changes in requirements or technologies. • Maintain composure and clarity of thought under pressure.

• Design and implement Agentic AI systems that power Coinbase’s customer support experience and compliance processes • Design a platform (primitives, orchestration platform, tools, etc.) that would allow other domains to quickly leverage Agentic AI to automate their processes • Interact with stakeholders in many different organizations that build Coinbase products or are responsible for financial operations • Participate in shaping of the strategy and execution path of Coinbase Platform • Manage individual projects priorities, deadlines and deliverables with your technical expertise • Mentor and train other team members on design techniques and coding standards • Write high quality, well tested code to meet the needs of your customers

• Evolve a modern, AI-first quality strategy for our fast-scaling SaaS architecture, including foundational infrastructure and emerging agentic/intelligent systems. • Integrate AI enhancements into CI/CD pipelines (e.g., predictive flakiness detection, automated test generation, self-healing scripts) to improve isolation, data setup, & execution reliability using existing/suggesting tools. • Establish scalable testing practices that support hyper-growth and petabyte-scale AI data pipelines. • Design deterministic and statistical testing approaches for non-deterministic LLM-based and agentic systems, addressing hallucinations, prompt injection, bias, drift, and safety risks. • Build automated evaluation pipelines and harnesses for correctness, faithfulness, retrieval quality, generation accuracy, tool-calling, planning sequences, and multi-agent flows. • Execute/Develop test frameworks for the full AI lifecycle: prompts, datasets, embeddings, model versions, RAG pipelines (end-to-end validation), and guardrails. • Implement red-teaming, bias/fairness checks, and compliance mechanisms; leverage in-trend frameworks for metrics and observability. • Partner closely with product, data science, AI engineering, and dev teams to test AI features, conduct multi-agent simulations, and ensure high-quality roadmap delivery. • Facilitate knowledge sharing and upskilling on AI testing best practices across the Quality Function. • Drive core metrics (DORA, test coverage/effectiveness) plus AI-specific indicators (e.g., hallucination rate, context precision, drift detection). • Build real-time dashboards and support A/B testing of models with post-deployment monitoring. • Champion a quality-first, ethical AI mindset organization-wide. • Mentor SDET’s, lead workshops on AI risks/validation, and influence design/deploy/incident processes. • Define roadmaps and best practices for sustainable AI quality assurance.