Backend Engineer

You'll own a large, customer-facing part of the product: how it's designed, how it's built, and how well it holds up once marketers depend on it. You'll make the technical decisions for that area, work with our Applied AI engineers on the agents and model systems behind it, and help earlier-career engineers do their best work. This is not a role for engineers who want to stay specialists in one language or one layer, for example, Java-only or Go-only. The work spans the whole stack and changes from week to week.

Responsibilities:

• Product areas: own one or more areas end to end, from technical design to performance and reliability in production.
• Customer problems: work with customers and customer-facing teams to decide what to build, and check after launch that it worked.
• AI features as systems: build retrieval, tool use and agents where they fit, plus monitoring and fallbacks for when the model gets it wrong.
• Evals: define evals for open-ended problems and scenarios, and use them to decide when a prompt, model or agent change is ready to ship.
• Scope and trade-offs: shape scope with product and design, push back when a simpler version would reach customers sooner, and choose when to build, buy or use an existing service.
• Quality and mentoring: set the bar in your area through code review, testing and design docs, and pair with earlier-career engineers on hard problems.

Requirements:

• 3-6 years building and shipping web products, with real ownership of systems customers rely on.
• Depth in both frontend and backend, for example, TypeScript, React, a Node or Python backend, document or SQL databases, and cloud infrastructure.
• Examples of technical decisions you made, the alternatives you rejected, and how they played out.
• Hands-on experience shipping at least one LLM-powered feature to real users.
• Time on a team known for its bar in experience, quality, reliability or scale, and the practices you brought away from it.
• Curiosity about what's changing in AI: you try new models and tools soon after they come out and have good judgment about which are worth adopting.
• Relevant experience may include:
• Working knowledge of Claude Code, Codex or Cursor.
• B2B products, including permissions, multi-tenancy, audit logs or integrations.
• Observability for AI features in production.
• Being an early engineer at a startup.

Skills

LLM, Large Language Models, Node, Python, React, TypeScript