FluidStack
Decision Engineer, Manufacturing & Supply Chain
About this role
Fluidstack is seeking a Decision Engineer to build AI-powered tools and decision systems for manufacturing and supply chain operations. You'll work end-to-end from conception to production, integrating frontier LLMs with internal systems to drive significant productivity gains across the organization.
What you'll do
- Build and deploy decision systems using frontier models that enhance manufacturing and supply chain productivity
- Own tools throughout their lifecycle, iterating directly with end users to ensure adoption and impact
- Design and maintain shared AI infrastructure including agent and skill frameworks
- Create custom integrations between LLMs and internal systems like Slack, GitHub, and operational APIs
- Monitor evolving AI capabilities and proactively identify opportunities to apply emerging techniques
- Translate technical solutions for non-technical stakeholders and drive internal tool adoption
What they're looking for
- Production software engineering in Go, Python, and TypeScript
- Full-stack web development with React, Next.js, or similar frameworks
- LLM API integration (OpenAI, Anthropic, open-weight models)
- AI coding agents (Claude Code, Cursor, or similar)
- MCP server creation and agentic frameworks
- Autonomous problem-solving and high-impact shipping
- Strong product and systems judgment
- Technical communication with non-technical audiences
Benefits
- Competitive cash and equity compensation
- Health, dental, and vision insurance
- Retirement plan
- Generous PTO policy
- Pay equity and transparency commitment
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FluidStack
FluidStack builds AI infrastructure at scale, developing data centers and warehouse operations designed to handle gigawatt-capacity compute deployment. The company is hiring for warehouse engineers, data center operations specialists, product engineers, and people leaders to support rapid infrastructure expansion across multiple sites.
- Website
- fluidstack.io
Likely interview questions
- Walk us through a specific tool or skill you've built using AI coding agents—what problem did it solve and how did you measure impact?
- Describe your experience integrating LLM APIs into production applications. What challenges did you encounter and how did you overcome them?