Product Engineer, Manufacturing

About this role

Join Fluidstack's Decision Team to build software that automates AI infrastructure manufacturing at scale. You'll create quality control systems, factory scheduling, integrator scoring, and defect tracking tools that turn manufacturing expertise into automated workflows across data center production.

What you'll do

• Build quality control software: inspection plans, FAT procedures, and automated spec verification with nonconformance tracking
• Develop self-replanning factory scheduling based on demand, BOMs, and real-time yield data across production lines
• Create continuous scoring system for third-party integrators using defect density, test rates, and corrective-action metrics
• Implement traceability systems connecting field defects back to design revisions, factories, and test history
• Embed with manufacturing teams to translate domain expertise into graph-based systems and queries
• Ship features end-to-end with minimal oversight while maintaining production-grade foundations

What they're looking for

• Production code in Go, Python, or TypeScript
• LLM APIs and agentic frameworks (OpenAI, Anthropic, Claude Code, Cursor)
• Full-stack product development with rapid iteration
• Domain modeling and translating expert workflows into software
• Manufacturing systems (MES, PLM, BOM management, scheduling optimization)
• Quality and compliance workflows (NCR, corrective actions, FAT procedures)
• Graph databases and data modeling
• Ability to work independently and ship without direction

Benefits

• Equity and competitive salary ($150,000–$275,000)
• Work on civilization-scale AI infrastructure with direct impact
• Collaborate with hardware, software, and domain experts
• Hands-on factory floor exposure and direct user feedback
• Opportunity to shape product direction and culture
• AI coding tools and modern development environment

Likely interview questions

• Walk us through a time you shipped production code without waiting for approval—what was the problem and how did you approach it?
• Describe your experience with LLM APIs and agentic frameworks. How have you used them to solve real problems?