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Kaizen Labs is a government technology company founded in 2022 that replaces legacy federal systems with modern, AI-native software. The company has grown 35x this year and serves 40+ million residents across 50+ agencies in 17 states. Based in New York City and DC with $35M in funding from NEA, a16z, Accel, and others, Kaizen is building systems of national consequence—from ticketing platforms for presidential libraries to procurement marketplaces for counter-drone systems to veteran benefits platforms.
In this role, you will own internal tools end-to-end, from problem discovery through architecture, build, rollout, and iteration. You'll build AI-powered internal systems similar to Bidbuddy (an internal tool that automated 8 hours/week of manual RFP scraping and analysis). Your responsibilities include:
- Owning internal tools across their full lifecycle, working directly with leads in Proposals, Customer Ops, Sales, Design, and Engineering to identify bottlenecks and translate vague asks into concrete builds
- Building AI-powered document parsing, agentic workflows, automated research routing, and automation that converts hours of manual work into minutes
- Driving AI enablement across the company, ensuring all employees can effectively use these tools
- Supporting the external analytics layer in Kaizen's platform to give customers actionable data
- Backing up the deployment team during customer implementations
You should have 2–5 years of engineering, data, or applied AI experience (or equivalent impact). You must have shipped something end-to-end that a team still uses—an internal application, automation, or tool that changed workflows. You need genuine fluency with AI tools (shipped, not just explored), strong SQL, data modeling skills, and the ability to scope problems correctly from non-technical stakeholders. Low ego and high output are essential; you'll do unglamorous rollout work because that's what makes builds matter.
Strong candidates may have built internal tooling at scale (getting hundreds to change workflows), come from forward-deployed or solutions engineering roles at AI/developer tools companies, know N8N/Zapier deeply, or have built in regulated environments like government or healthcare.
