Staff Backend Engineer, AI Agent Platform

About the role

Some of the things you’ll do:

• Own the architecture of the agent platform: how any team at Quo creates and runs an agent, the shared pool of tools and integrations agents call, the single layer every model call goes through, and the execution engine that turns model output into real API and tool calls.
• Build advanced orchestration, from one agent coordinating five or more subagents to long-running agents that work on a task for hours.
• Design memory and self-learning, so our agents get better from customer feedback over time.
• Drive the scale, performance, reliability and cost of agent execution as usage grows across calls, texts and the workspace.
• Make it easy for other teams to build on the platform. You'll be its best advocate, with clear docs, good defaults and a lot of pairing.
• Mentor engineers through design reviews, code reviews and hands-on coaching, raising the bar for the whole AI domain.
• Stay close to what's happening in LLMs and agent tooling, and turn what you learn into practical roadmap proposals.

Tech Stack & Tools:

• Node and TypeScript on the backend
• Event-driven microservices with Temporal and RabbitMQ at the centre
• Kubernetes on AWS
• Postgres, MongoDB, Elasticsearch and Redis
• Datadog for observability
• Web (React), iOS and Android clients on the other side of the API

About you

• You've built and scaled the backend of a product with a lot of real users, and you've led the architecture of business-critical services.
• You've shipped LLM-powered systems to production, ideally an agent platform or orchestration layer. If you haven't built an agent platform yet but have built something close, like an ML platform, a workflow engine or real-time communications infrastructure, and you've gone deep on LLMs hands-on, we'd still love to talk.
• You're strong in TypeScript and Node, or you're fluent in another backend language and keen to ramp up on ours.
• You have high agency. You see what needs doing, pick it up and move it forward without waiting to be asked.
• You learn fast and you're plugged into the AI world. You know what's changing in models, agent frameworks and tooling, and you have opinions about it.
• You write clear design docs, explain trade-offs well and bring other engineers and teams along with you.
• You care about customers. You weigh technical decisions against the experience of a small business owner using our product every day.

Bonus points if you:

• Have run Temporal or another durable workflow engine in production.
• Have worked on customer-facing AI products, not only internal developer tooling.
• Have founded or been early at a startup.

Compensation

• SF Bay Area, Los Angeles, Seattle, Portland, Boston, New York and Washington DC Metro: $205,000 to $242,000 USD
• All other US locations: $186,000 to $217,000 USD
• Canada: $189,000 to $222,000 CAD