Backend + AI Engineer

🧠 About Feather Feather is building AI agents that do real work for enterprises. Not chatbots. Not IVR. Not copilots. Autonomous agents that can reason, take actions, communicate across channels (voice, text, email), and complete end-to-end business workflows. We operate at the intersection of LLM reasoning, real-time communication, and production system orchestration — enabling companies to deploy AI employees across sales, support, operations, and collections. Feather is building the infrastructure and runtime layer that makes autonomous agents production-ready across communication channels. We’re backed by leading investors and already at $1M+ ARR, scaling quickly into enterprise deployments. 🔧 What You’ll Do Agent Runtime & Cognition Systems Design and build the core runtime that powers autonomous AI agents Architect systems for reasoning loops, planning, tool use, and memory Enable agents to execute multi-step workflows across business systems Multi-Channel Communication Infrastructure Build systems enabling agents to operate across voice, SMS, chat, and email Develop real-time conversation pipelines and turn management Handle interruptions, context switching, and long-running dialogues Orchestration & Workflow Execution Develop agent orchestration layers for async and long-lived tasks Build DAG/workflow systems coordinating agent decisions and actions Enable outcome-based automation (not just conversations) Applied LLM Systems Integrate frontier models into production agent systems Build prompt pipelines, evaluation harnesses, and guardrails Design reliability layers: fallbacks, retries, human handoffs Distributed Systems at Scale Architect event-driven systems handling millions of agent actions Build job queues, schedulers, and execution pipelines Optimize latency, throughput, and infrastructure cost 🧩 What We’re Looking For Core Engineering Depth 3–7 years building scalable backend or distributed systems Strong experience in Python or TypeScript Deep understanding of async processing and event-driven systems Experience designing production APIs and service architectures Agent / AI Systems Exposure Experience working with LLM APIs in production environments Familiarity with agent frameworks, reasoning systems, or tool use Built systems where AI drives real user or business outcomes Systems Ownership Mindset Comfortable owning infra end-to-end Strong debugging and performance optimization skills Product-minded — you think in workflows, not endpoints 🌟 Bonus Points Built agent tooling (memory, planning, tool execution) Experience with workflow engines or orchestration systems Familiarity with real-time communication infra Experience with RAG, knowledge bases, or retrieval systems Exposure to eval frameworks and agent reliability testing Worked on customer ops, sales, or support automation 🏗️ Tech Stack LLMs: OpenAI + frontier / open-weight models Agent Systems: Custom runtimes + orchestration frameworks Communication: Voice, SMS, chat, email infrastructure Backend: Python, TypeScript Infra: AWS, Kubernetes, Postgres, Redis Observability: Prometheus, Grafana, tracing Workflows: Queue + DAG orchestration systems 💼 What We Offer Competitive salary + founding equity Direct ownership of core platform architecture Work on frontier agent infrastructure problems Build AI systems deployed in real enterprise workflows Fast shipping velocity with technical founders 🎯 Who This Role Is For Engineers who want to: Build AI agents that take actions — not just generate text Work on reasoning systems, orchestration, and autonomy Design infrastructure for AI employees Shape the foundation of an emerging category