Wonderschool

Wonderschool

Full Stack Software Engineer, Applied AI

San Francisco

Sponsorship not specifiedDetected 86 days ago
TypeScriptRubyElixirReactNode.jsRailsFull-Stack DevelopmentCode ReviewPostgreSQLCloud PlatformsCI/CDDevOpsAPI DevelopmentGraphQLRESTAgentic AICustomer SuccessCommunication

About the role

  • We started with childcare providers because it is a large, underserved market where technology can have an outsized impact on revenue.
  • OpenClaw agents run most of our operations: provider outreach, customer communications, enrollment workflows, compliance tasks.
  • Non-engineers at Wonderschool spin up automations, cron jobs, and API workflows directly from Slack, without touching a line of code.

Responsibilities

  • Own the AI Development Loop
  • Build and maintain the automated product pipeline: signal detection, agent-generated requirements, AI-driven development, AI code review, commit
  • When agents break in production, you own the diagnosis and fix
  • Build tools and workflow infrastructure that let operations, sales, and customer success teams operate the platform themselves, without filing engineering tickets
  • Build features across the frontend and backend: React/TypeScript on the front, Node.js or Elixir on the back, Postgres underneath
  • Own the full lifecycle from requirements to production
  • making AI agents reliable enough to build the product itself.

Requirements

  • 5+ years of experience as a full stack engineer
  • Hands-on experience with AI coding agents (Claude Code, OpenClaw, Codex, Cursor, or similar) in a real development workflow
  • Proficiency with React/TypeScript and at least one backend language (Node.js, Elixir, Ruby on Rails, or similar)
  • Experience with Postgres or similar relational databases

Nice to have

  • Experience designing feedback loops that improve AI output quality over time
  • Background at a startup or high-growth company
  • Experience with CI/CD, DevOps, or cloud infrastructure
  • Experience with REST or GraphQL API design
  • This is not a 9-to-5 role.
  • At this stage, getting AI agents to ship reliable production code requires close observation of what is actually happening in the codebase.
  • When the system is working well, that monitoring is lightweight and mostly automated.
  • Getting it to that point requires genuine commitment: watching the system, catching failures before they compound, and iterating fast.

Skills

  • Translate what internal teams need into automated, reliable systems
  • Help non-engineers understand what is possible and then make it happen
  • Clear, direct communication skills, written and verbal

Compensation

  • $140,000+, depending on experience, skills, and location.
  • This role is also eligible for equity and other forms of compensation.

Benefits

  • This role requires a high degree of personal accountability for system health.
  • Health benefits with up to 100% coverage for employee premiums and up to 80% for dependents
  • Wifi and employee wellness stipends
  • Flexible PTO, paid holidays, and mental wellness days
  • Competitive parental leave (eligible after 6 months of employment)
  • Wonderschool offers a competitive benefits package, including:

Company info

  • we are building a platform and a playbook that will expand into other verticals.
  • We are heavy users of AI.
  • a system where a user signal (a provider revenue dropping, an enrollment bottleneck, a churn spike) automatically triggers a chain of AI agents that writes the product requirements, designs the solution, builds the code, reviews it, and ships it.
  • No human in the loop for the routine stuff.
  • Humans define what matters, train the agents, and review edge cases.
  • We are not there yet.
  • You are the person who helps us get there, piece by piece.
  • watching the system, catching failures before they compound, and iterating fast.
  • We are also not looking for engineers who want to write code in isolation.
  • Compensation and Benefits

This listing is sourced directly from Wonderschool's careers page and normalized into a canonical job model.