AI Agent Infrastructure Lead

Leads development of internal AI agent infrastructure ("Goose") to boost velocity across engineering, ops, and other teams. Builds safe, autonomous agent workflows for codebase inspection, testing, and complex tasks with strong focus on safety and accuracy.

About the job

What You'll Do

Within days

Ship AI agent features that help Sphere's engineering team use agents more effectively and responsibly.
Iterate on versions of Sphere's agent sandbox environments.
Create workflows where agents can inspect the codebase, run local infrastructure, make changes, run tests, and prepare work for human review.
Work directly with engineers to identify high-leverage internal workflows where agents can create immediate velocity.

Within months

Lead Sphere's internal efforts to enable AI agents to act more autonomously across engineering, ops, customer success, tax research, and implementation.
Build "Goose" for Sphere: the internal AI agent layer that helps agents understand and operate across Sphere's systems.
Own the infrastructure, tooling, and workflows that let agents safely take on more complex internal work over time.
Establish the patterns for how Sphere uses agents internally, including context, permissions, review, observability, and escalation.

Requirements

Experience building production-quality software.
Experience in AI agents, coding agents, internal developer tooling, or AI agent enablement.
Comfort working across backend systems, infrastructure, local development environments, CI, and internal tools.
Strong judgment around autonomy, safety, permissions, and human review.
High agency. You can take a vague internal problem and turn it into a working system people actually use.
Strong attention to detail. Agents are only useful here if they improve speed without reducing correctness.

Skills

AI Agents, Coding Agents, Backend Systems, Infrastructure, CI/CD, Internal Developer Tooling, Sandbox Environments, Observability, Permissions Management, Local Development Environments