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Superconductor

Parallel AI coding agents platform

Agent Maestro

AI Application EngineeringConfirmed live in the last 24 hours
Full-time

About the Job

Requirements

  • The candidate must have experience building multi-agent systems or complex large language model-powered applications in production.
  • The candidate must be comfortable with Python, TypeScript, and Ruby.
  • The candidate must be able to reason about failure modes, edge cases, and graceful degradation as a systems thinker.
  • The candidate must be self-directed and experimental, able to build prototypes without waiting for a roadmap.
  • The candidate must have at least 3 years of professional engineering experience, including at least 1 year deeply focused on artificial intelligence or large language model applications.

Responsibilities

  • Build intelligent orchestration that assigns the right type of agent, such as a product manager, coder, quality assurance agent, or deployer, to the right task.
  • Develop self-healing workflows that detect when agents are blocked, diagnose issues, and resolve them automatically or escalate them to humans.
  • Experiment with new models, agent frameworks, prompting strategies, and tool-use patterns.
  • Build evaluation and benchmarking systems and improve their results.
  • Produce high-quality, clean, human-reviewable code and review substantially more code than is written.

Desired Qualifications

  • Experience building or contributing to an agent framework, Model Context Protocol server, or agent evaluation benchmark.
  • Experience shipping a product where artificial intelligence agents operate autonomously.
  • Experience with Rails.

About the Company

Superconductor

Parallel AI coding agents platform

Superconductor is a cloud platform that lets software teams run multiple AI coding agents in parallel on the same project, compare results side by side, and ship the best implementation. Agents run in isolated containers with configurable network access, and each parallel run has a live browser preview and a built-in diff viewer for quick testing and review; teams can iterate by chatting with a promising agent and push the chosen solution via GitHub PRs or Slack/GitHub workflows. Unlike tools that run a single AI agent, it treats code generation as a set of competing options from different providers and emphasizes sandboxing and parallel exploration. Its goal is to help teams move faster and make better coding decisions by integrating parallel AI-generated code into standard development workflows.

Company size
1-10
Company stage
Not listed
Total funding
Not listed
Headquarters
San Francisco, CA
Founded
2025

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