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FluidStack

Software Engineer, Product

San Francisco, CA$208k–$269kfulltimemidAdded today

About this role

Fluidstack seeks an AI Software Engineer to build internal tools and shared infrastructure that multiply team productivity using frontier AI models. You'll integrate LLMs across the organization's systems and establish AI capabilities that drive measurable impact across engineering, operations, and business functions.

What you'll do

  • Develop internal tools using frontier LLM models to automate workflows and reduce manual work
  • Design and maintain shared AI infrastructure including agent and skills systems
  • Integrate AI capabilities into existing products and internal tools with measurable outcomes
  • Build custom integrations between LLMs and systems like GitHub, Slack, Notion, JIRA, and Salesforce
  • Research emerging AI techniques and identify opportunities to apply them to organizational challenges
  • Create documentation and support teams in adopting and extending AI tools

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)
  • MCP servers and agentic framework development
  • AI coding agents (Claude Code, Cursor, Antigravity)
  • Autonomous problem-solving and systems thinking
  • Product design taste and user-focused development
  • Cross-functional communication and technical translation

Benefits

  • Competitive total compensation including cash and equity
  • Health, dental, and vision insurance
  • Retirement plan
  • Generous PTO policy
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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
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Likely interview questions

  • Describe a time you shipped an AI-powered feature end-to-end—how did you validate it drove real productivity gains?
  • Walk us through your experience building with LLM APIs and agentic frameworks. Which have you used and what were the tradeoffs?
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