FluidStack

Product Engineer, Business Operations

San Francisco, CA$150k–$275kfulltimemidAdded today

About this role

Fluidstack is building AI infrastructure at civilization scale and needs a Product Engineer to automate the financial and operational systems that deliver gigawatts of compute. You'll bridge product, engineering, and business operations by shipping AI-powered software that turns capital planning, contract management, and accounting into automated, continuous processes.

What you'll do

Build automated capital pipeline systems that gate fund release on verified delivery milestones
Create AI-powered contract parsing systems to extract obligations, prices, and penalties from MSAs and legal documents
Design continuous accounting close systems that track assets from purchase through deployment to service
Embed with finance, treasury, and legal teams to translate their workflows into structured data and automated workflows
Ship end-to-end solutions for complex business operations problems without waiting for approval
Develop financing drawdown automation that assembles proof chains for capital releases

What they're looking for

Production code in Go, Python, or TypeScript
LLM API integration (OpenAI, Anthropic, or open-weight models)
Agentic frameworks and MCP servers
AI coding tools (Claude Code, Cursor)
ERP and fixed asset accounting
Contract lifecycle and legal document processing
Capital planning and FP&A
Structured finance or treasury operations

Benefits

Competitive cash compensation: $150,000–$275,000
Work on civilization-scale infrastructure for AI
Autonomy to find, design, and ship solutions end-to-end
Collaboration with experts across finance, legal, and operations
Fast-moving company focused on speed and impact
San Francisco, CA location

Likely interview questions

Tell us about a time you found a problem no one assigned you and shipped a solution end-to-end—what was blocking others from doing it?
Walk us through a recent project where you used LLMs or agents to solve a real business problem. How did you measure success?

Describe your experience building software that non-engineers actually adopt. How did you earn their trust?
Have you worked with financial systems, contracts, or accounting workflows? What was the hardest part to automate?
How do you balance moving fast with building foundations that don't constrain the team later?
Tell us about your experience with AI coding tools like Cursor or Claude Code. How has it changed your workflow?
What excites you about working on infrastructure for AI, and what concerns do you have about the space?

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