Role Overview
At Ripple, we’re building a world where value moves like information does today. It’s big, it’s bold, and we’re already doing it. Through our crypto solutions for financial institutions, businesses, governments and developers, we are improving the global financial system and creating greater economic fairness and opportunity for more people, in more places around the world. And we get to do the best work of our career and grow our skills surrounded by colleagues who have our backs.
If you’re ready to see your impact and unlock incredible career growth opportunities, join us, and build real world value.
Are you an ambitious engineer looking to make an outstanding impact in the world of financial technology? At Ripple Labs Inc., we are seeking a GenAI Platform Staff Software Engineer to join our team in San Francisco, CA. You will collaborate with world-class colleagues to successfully implement innovative solutions that redefine the global financial system.
What You’ll Do
- Develop and deliver production-quality agentic AI systems end-to-end using Python, Go, and/or Java, covering EKS deployment, agent runtimes, memory systems, orchestration, tool integration, and evaluation pipelines that operate across Ripple's polyrepo engineering environment.
- Define and advance Ripple's Enterprise Agentic AI and developer platform architecture through practical implementations, reference systems, and production deployments — not abstract diagrams.
- Build and implement multi-agent orchestration patterns (planner, executor, reviewer, tool agents) using frameworks such as LangGraph, MCP, Claude Code agent harnesses, or similar orchestration systems, with strong regression coverage and observability.
- Run fast, high-quality POCs on emerging agent architectures; harden successful patterns into reusable platform services, APIs, SDKs, and developer templates that engineering teams across Ripple can adopt.
- Architect and implement data flywheels that continuously improve agent quality through telemetry, benchmarking, automated evaluation, and structured feedback loops — treating quality, cost, latency, and safety as first-class signals.
- Embed security, guardrails, sandbox isolation, auditability, and policy enforcement directly into agent runtimes in partnership with security, compliance, and governance teams — particularly for workflows that touch XRPL, RLUSD, and payment systems.
- Evaluate, integrate, and extend open-source and third-party agent platforms; drive disciplined build-vs-use decisions based on performance, scalability, control, and long-term platform ownership.
- Collaborate closely with engineering, infrastructure, product, and business partners to align architectural direction with enterprise priorities and accelerate adoption across the organization.
What You'll Bring
- Bachelor's degree in Computer Science or related field or equivalent experience; Master's or PhD preferred.
- 10+ years of experience building and shipping large-scale distributed systems with significant hands-on coding in Python, Go, Java, or similar systems languages.
- Proven ability to move quickly from idea to functional prototype to robust, scalable platform solution.
- Proven track record in constructing agentic AI systems, including RAG pipelines, long-lived memory models, multi-agent orchestration (e.g., AgentCore, Strands, LangGraph, MCP, Claude Code, LangChain), tool frameworks, and evaluation infrastructure.
- Expert-level depth in Kubernetes (EKS preferred), service mesh (Istio), containerized workloads, networking, APIs, and secure enterprise integration patterns.
- Experience crafting benchmarking, regression testing, telemetry, and observability systems (OpenTelemetry, Prometheus, Grafana) that measure agent quality, latency, cost, reliability, and safety.
- Strong understanding of performance tuning in hybrid environments, including managed inference endpoints and GPU-based workloads.
- Excellent collaboration skills with the ability to influence cross-functional partners, build positive relationships, and clearly communicate complex architectural concepts to both technical and business audiences.
Preferred Qualifications
- Proven experience delivering reusable developer-acceleration components such as SDKs, APIs, templates, reference implementations, and CI/CD automation.
- Experience building cross-repo context engines or AI-native developer platforms at scale (vector databases such as Qdrant, code embeddings, Tree-sitter, retrieval across thousands of repositories).
- Experience integrating enterprise agentic search and orchestration platforms (Glean, Cursor, Claude Code at organizational scale, Microsoft Copilot Studio, or similar).
- Experience embedding fine-grained policy enforcement, access controls, sandbox isolation, and audit trails directly into AI runtimes — particularly in regulated or financial contexts.
- Familiarity with formal methods (TLA+, model checking) or property-based testing applied to agentic systems and distributed protocols.
- Background in blockchain, payments infrastructure, or the XRPL/RLUSD ecosystem.
- Evidence of meaningful open-source contributions: core commits, maintainer-ship, widely adopted libraries, or public technical artifacts demonstrating system-level depth.