Senior Software Engineer, Backend (AI Agent Runtime)

About the role

• Build real-time AI agent infrastructure: Design and operate the stateful, low-latency runtime that powers voice and chat AI agents — from LLM streaming and conversation state management to graceful recovery and multi-channel support.
• Solve distributed systems problems: Own session management across scaled-out workers — including affinity, checkpointing, crash recovery, and consistency under concurrent access.
• Build a function execution platform: Own a serverless-style runtime where customers deploy custom logic — build orchestration, container lifecycle, autoscaling, and versioned rollouts.
• Own developer experience and test infrastructure: Build CLI tools, local development environments, and test execution frameworks that let engineers iterate quickly and ship with confidence.
• Raise the bar on production quality: Drive observability, incident response, and engineering best practices across the team.

What we’re looking for

• 5+ years of software engineering experience, with meaningful time spent on infrastructure, platform, or systems work.
• Strong Python and Go — both are core to this role, not one primary and one secondary.
• Deep understanding of distributed systems: consistency, fault tolerance, state management, concurrency.
• Experience with Kubernetes and cloud-native infrastructure.
• Experience building developer-facing tooling — CLIs, SDKs, local dev environments, or internal platforms.
• Strong communicator who can drive technical decisions, write clear design docs, and mentor others.
• High bar for code quality — thorough testing, thoughtful code review, and sustainable engineering practices.
• Comfort operating what you build — on-call, incident response, and production ownership.
• AI-native workflow — you actively use LLMs and AI-assisted tools in your daily development, and can leverage them to move faster and tackle problems that would otherwise be impractical.

Nice-to-haves

• Experience with real-time voice or streaming media systems.
• Hands-on with LLM integration — streaming inference, prompt orchestration, retrieval-augmented generation.
• Experience building serverless or function-as-a-service platforms.
• Workflow engines (Temporal, Argo, Airflow) for durable, long-running processes.
• Experience in conversational AI or speech domains.
• Infrastructure-as-code and GitOps workflows.