FluidStack

Full-Stack Software Engineer

San Francisco, CA$150k–$350kfulltimemidAdded today

About this role

Fluidstack is building civilization-scale AI compute infrastructure and seeks a Full-Stack Software Engineer to own end-to-end features across their technology stack. You'll work with high autonomy to ship software that accelerates datacenter deployment, from supply chain automation to capacity forecasting and operational workflows, leveraging AI tools and modern development practices.

What you'll do

Own features across the full stack from design to deployment, with no handoffs or approval delays
Build supply chain automation interfaces for GPU and component ordering at scale
Develop capacity forecasting and scheduling systems to model timelines and predict bottlenecks
Create internal AI infrastructure including agent frameworks and cross-platform integrations
Design operational automation and self-service tooling to eliminate manual toil
Build applications for datacenter design validation, simulation, and power topology modeling

What they're looking for

Production software engineering in Go, Python, or TypeScript
LLM API integration (OpenAI, Anthropic, open-weight models)
AI coding tools (Claude Code, Cursor, etc.) and agentic frameworks
MCP (Model Context Protocol) server creation and usage
Systems thinking and full-stack architecture
High-autonomy problem-solving and product design taste
Technical communication with non-technical stakeholders
Rapid iteration balanced with sustainable foundation-building

Benefits

Base salary $150,000–$275,000 depending on experience and location
Meaningful equity to share in long-term company performance
Health, wellness, and other benefits [details not fully specified]
Flat organizational structure with no middle management
Autonomy to ship directly without escalation or approval delays
Work on civilization-scale AI infrastructure challenges

Likely interview questions

Tell us about a time you shipped a complex feature end-to-end with minimal direction—how did you identify the problem and validate success?
Describe your experience building with LLM APIs. What agentic frameworks have you used, and how did you handle autonomous agent failures?

How do you approach using AI coding tools like Cursor or Claude Code to accelerate development? Give a specific example.
Walk us through how you'd design a system to automate GPU supply chain ordering for 10,000+ units. What would be your first priorities?
Tell us about a time your product or design taste led you to push back on a technical decision. What was the outcome?
How would you balance shipping capacity forecasting features quickly while building a foundation that operations teams can extend long-term?
What excites you about AI alignment and compute infrastructure specifically, and why does this problem matter to you?

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