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Ambrook

Software Engineer, Labs

San Francisco (Remote)$115k–$280kfulltimemidAdded 1 month ago

About this role

Ambrook Labs is building AI-powered financial tools for family-run businesses in agriculture, construction, and other underserved industries. You'll run rapid 1-2 week experiments with real customers, prototype and ship end-to-end, and help define what industry-specific business software looks like when built with domain depth from the start.

What you'll do

  • Execute rapid experimentation cycles: prototype, ship to real customers, learn, and iterate within 1-2 weeks
  • Collaborate directly with operators across agriculture, construction, trucking, and similar industries to validate problems
  • Build evaluation frameworks that enable the team to move quickly while maintaining confidence in results
  • Create internal demos and external write-ups to communicate learnings and insights
  • Determine which experiments warrant scaling and help the team build conviction on where to invest resources
  • Contribute to operational decisions about the team's direction and approach

What they're looking for

  • Shipped AI/LLM-powered features to real users
  • Full-stack development: frontend, APIs, prompt engineering, and evaluations
  • 0-to-1 product building with comfort in ambiguity and scoping
  • Self-directed problem identification and execution
  • Technical writing and communication (internal and external)
  • Portfolio of side projects, open source, or similar demonstration of idea generation
  • Curiosity about underserved industries with demonstrated action
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Ambrook

Ambrook builds AI-powered financial software for family-run businesses in agriculture, construction, and other underserved industries, featuring intelligent document parsing, transaction automation, and modern financial infrastructure. The company is hiring full-stack engineers, design engineers, AI specialists, and recruiting talent to scale its product and team.

Website
ambrook.com
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Likely interview questions

  • Walk us through an AI/LLM feature you shipped to real users. What was the hardest part—was it the model, the eval, or something else?
  • Describe a time you built something 0-to-1 with significant ambiguity. How did you decide what to build and how did you know when to pivot?
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