Senior Software Engineer, AI Platform

About Decagon Decagon is the leading conversational AI platform empowering every brand to deliver concierge customer experiences. Our technology enables industry-defining enterprises like Avis Budget Group, Block’s Cash App and Square, Chime, Oura Health, and Hunter Douglas to deploy AI agents that power personalized, deeply satisfying interactions across voice, chat, email, SMS, and every other channel. We’re building a future where customer experiences are being redefined from support tickets and hold music to faster resolutions, richer conversations, and deeper relationships. We’re proud to be backed by world-class investors who share that vision, including a16z, Accel, Bain Capital Ventures, Coatue, and Index Ventures, along with many others. We’re an in-office company, driven by a shared commitment to excellence and velocity. Our values — Just Get It Done, Invent What Customers Want, Winner’s Mindset, and The Polymath Principle — shape how we work and grow as a team. About the Team The AI Platform team builds the foundational systems that enable every engineer at Decagon to build with AI. Our users are both engineers and coding agents. We build the execution environments, agent harnesses, evaluation systems, developer tools, and shared abstractions they use to plan, write, test, review, and ship production software. We sit within Infrastructure and work closely wit [... source excerpt omitted ...] an experienced software engineer to help build Decagon’s AI-native developer platform. You’ll work at the intersection of distributed systems, developer tooling, and applied AI. You will experiment with new agentic workflows, identify where they create real leverage, and turn the best ideas into dependable systems used across the engineering organization. This is a highly product-oriented platform role. You’ll work directly with engineers to understand how they build software, find high-value opportunities for automation, and own solutions from architecture and implementation through rollout, adoption, and production operation. A few areas you may work on: Agent execution: B [... source excerpt omitted ...] tems, and the tools needed to complete real engineering work. Agentic development workflows: Apply AI across planning, technical design, implementation, testing, code review, and production operations. Evaluation and validation: Build systems that test and review human- and agent-generated changes, catch regressions earlier, and improve confidence in autonomous development. Agent-native developer infrastructure: Design CI/CD, local development, APIs, CLIs, and platform tooling that can be operated directly by both engineers and agents. Internal builder platforms: Create reusable frameworks that let teams quickly build their own internal tools, dashboards, automations, and agent