Engineer in Residence, Enterprise Agents

Large companies run on mission-critical internal systems that are often brittle, fragmented, and difficult to expose safely to modern AI. Security teams say the main obstacle is not detecting risk. It is getting internal databases, APIs, and legacy systems in front of an LLM stack without a months-long integration cycle. This role is to build secure AI agents that operate behind the corporate firewall while respecting existing permissions, controls, and operational boundaries.

What You'll Build

An agent framework that connects securely to internal databases, APIs, file systems, and legacy interfaces behind corporate firewalls.
A task-orchestration layer that turns operator intent into safe, reviewable, multi-step actions across internal systems.
An authentication and access-control layer that integrates with enterprise identity systems and preserves existing permission boundaries.
A monitoring and audit system that records every agent action for compliance, debugging, approval, and rollback.

What You'll Do

Own the build across agent architecture, connectors, orchestration, access control, auditability, and deployment.
Design for constrained network environments where cloud-first assumptions, latency, and data movement require deliberate tradeoffs.
Build the natural-language task layer and connectors needed for a focused enterprise workflow.
Implement security primitives such as credential isolation, least-privilege execution, action sandboxing, approval gates, and rollback.
Work with AI Fund's build team and enterprise users to validate the first wedge against real internal-tooling pain.

What You Need

Strong backend engineering ability across production systems, enterprise integrations, databases, APIs, authentication, and networking constraints.
Demonstrated experience building GenAI systems with agentic architectures, tool use, function calling, evals, and guardrails.
Experience designing systems where security, identity, permissions, auditability, and reliability are product requirements.
Product judgment for deciding which enterprise actions can be automated and which require human approval.
You know how to use AI coding assistants and modern AI tools to accelerate implementation without weakening system discipline.
US work authorization. We are unable to sponsor visas for this role.

Helpful But Not Required

Experience with MCP or similar tool-use frameworks for AI agents.

Experience building internal developer platforms, middleware, ETL systems, or enterprise integration products.

Founder or founding engineer experience building infrastructure software.

Who This Is For

A builder who sees enterprise internal tooling as an underserved, high-impact problem space.

Someone who understands that integration, authentication, permissions, and fragile dependencies determine whether the AI is useful.

What To Know Upfront

This is a 12-week, full-time, on-site residency in Mountain View, California.
Not every residency becomes a company. The goal is to pressure-test the idea quickly and honestly with real users and customers.
You will be building an AI Fund idea, not bringing your own startup idea into the program.
The process typically includes a Builder Event or equivalent working conversation, then a 48-hour Builder Challenge, then panel review with AI Fund build leadership.
The compensation is intentionally modest during the residency because the upside, if the idea works, is a founder-level role.

Compensation

$10,000/month for 12 weeks ($30,000 total). This is a contract role during the residency. If the build leads to a funded company, the next step is a founder-level role with meaningful equity upside.

We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.

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