Founding Engineer

tldr: Kiloforge is looking for exceptional generalist founding engineers (full-stack, AI, etc.) who are scrappy, have taste, and love building things to create the next generational company.

what is Kiloforge:

We’re building a new kind of company.

Kiloforge is a company factory: a system that autonomously ideates, validates, builds, and distributes software products. Before AI, there were countless niche communities and problems that had no way of being served with first class, high quality software. As software trends towards zero cost, solving these problems is now economically feasible. Kiloforge will create and operate thousands of high quality apps each serving a specific need eventually taking over all gaps in the application layer of software.

who you are:

• very generalist with your skillset (even potentially outside of engineering)
• have built things real people use
• very scrappy, quick on your feet, and resourceful
• strong product and design taste
• thrive in chaos
• love learning pretty much everything
• have founded a company before/have founding aspirations

who we are:

• we have founded companies before including one that scaled to 20M ARR
• have built dozens of products with thousands of users
• obsessed with getting the most out of life and making the biggest impact possible
• small, fast, team of founders/builders
• backed by a16z, uncork capital, partners from yc, and Rahul Vohra

why join us:

• we will bring in designers, creatives, and marketers of all kinds to encode their expertise in AI workflows. So this is a unique opportunity to learn across many domains and occupations
• you want to take a risk and change the way software is made forever
• you'll learn how to think about, build, and distribute real products in an AI-first way

what you'll do:

• build and ship micro apps every week with real users, encoding your processes into better and better automated workflows until it is fully automated
• work in close collaboration with the rest of the founding team
• run rapid experiments and decisively double down on what works while cutting what doesn’t