Product Engineer, Infrastructure Deployment

About this role

Fluidstack is building AI infrastructure at scale and seeks a Product Engineer to develop software that automates data center deployment workflows. You'll create a knowledge graph system that transforms site bring-up processes—from contractor scheduling to QA verification to closeout—turning manual coordination into automated, intelligent systems.

What you'll do

• Build a deployment knowledge graph that captures real-time site data (racks, cabling, testing results) as structured, queryable information
• Encode site sequencing rules and dependencies into software that auto-generates contractor schedules and detects delays early
• Develop automated inspection and checklist generation from design specs with verification against actual results
• Create vendor performance tracking and contractor bidding workflow automation based on measured speed and QA metrics
• Build closeout automation that assembles final documentation, as-builts, and root cause analyses from the graph
• Partner with deployment engineers and field teams to validate systems on live sites

What they're looking for

• Production code shipping (Go, Python, or TypeScript)
• LLM API integration and agentic frameworks
• AI coding tools (Claude, Cursor) and autonomous agent workflows
• Systems design for high-velocity, complex domains
• Cross-functional communication with non-software stakeholders
• Data modeling and graph database patterns
• Structured cabling or fiber networking (bonus)
• DCIM or infrastructure tooling experience (bonus)

Benefits

• Competitive salary ($150k–$275k based on geography and experience)
• [Additional benefits not specified in description]
• Work on civilization-scale infrastructure for frontier AI
• Direct collaboration with field engineers and deployment teams
• Opportunity to shape a critical bottleneck in AI infrastructure

Likely interview questions

• Walk us through a time you spotted a problem no one assigned you, designed a solution, and shipped it to production with minimal guidance.
• Tell us about your experience building with LLM APIs or agentic frameworks—what did you ship and what challenges did you solve?