> Markdown version of [/jobs/ext/3451289-software-engineer-ai-platform](https://www.wearedevelopers.com/jobs/ext/3451289-software-engineer-ai-platform). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md).

---

# Software Engineer, AI Platform

- **Company:** Fab 2  Inc
- **Location:** Austin, TX, United States
- **Salary:** $140,000.0 - $200,000.0
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Software Debugging, Programming Tools, Scientific Computating, Software Construction, Backend, AI Platforms, Machine Learning Operations, Front End Software Development
- **Published:** September 1, 2026
- **Apply:** https://www.indeed.com/viewjob?jk=f963908a877d0410

## About the Role

* Strong software engineering fundamentals and experience shipping production systems.
 * Comfortable working across frontend, backend, infrastructure, and data.
 * Effective with AI coding tools while still able to read, write, debug, and reason about code independently.
 * Strong product instincts and an ability to turn ambiguous problems into working systems.
 * Strong technical communication and a high degree of ownership.

Nice-to-have

 * Founding engineer, CTO, or early-stage startup background.
 * Experience building AI agents, developer tools, evals, or ML infrastructure.
 * Hands-on work with GPU or on-prem compute infrastructure.
 * Exposure to robotics, hardware, scientific computing, or automated experimentation.
 * Experience building software for regulated environments.

## Description

We're looking for a Software Engineer to build the AI platform behind fab2.

This is a greenfield role with broad ownership across our AI stack. You'll build the infrastructure and agent systems that turn engineering knowledge and workflows into useful tools for teams across fab2.

Responsibilities

 * Build the infrastructure for using AI models across cloud providers and our own on-prem compute.
 * Develop agents, shared skills, MCP servers, and sandboxed execution environments.
 * Create evals and benchmarks to measure where agents work, where they fail, and how to improve them.
 * Turn engineering knowledge and repeatable workflows into shared AI tools teams can build on.
 * Develop AI systems for automated experimentation, defect detection, engineering analysis, and fab operations.
 * Explore systems that can analyze results, propose what to do next, and eventually interact with physical equipment.
 * Help shape how AI gets used across Fab2 and inside the fab.