Join an early-stage startup.
Accelerate economic opportunity.
Join us as a Backend Engineer to build and scale the infrastructure that powers financial access for millions of underserved Americans. This role is for you if you want to own critical systems end-to-end and care deeply about performance, reliability, and scalability. You'll be expected to aggressively leverage AI-assisted development tools to dramatically accelerate your output and maximize impact, all while maintaining strong code quality and system reliability.
About us
Kovo is expanding access to financial tools for people historically excluded from the traditional credit system. We believe everyone deserves the opportunity for economic independence, and we’re building products that help people establish financial identity and unlock opportunity.
Over the past three years, Kovo has grown revenue 20x while maintaining exceptional capital efficiency. We rank above the 99th percentile in revenue-per-employee benchmarks, reflecting the leverage of a small, high-performing team.
The team combines the ambition of a venture-scale company with the discipline of a profitable business. We reached positive free cash flow early in our lifecycle, allowing us to grow with less dilution and focus on building for the long term.
Kovo is backed by Inspired Capital and Homebrew, and built by alumni from Plaid, Experian, Equifax, TransUnion, Innovis, Chime, Honey, Possible, Monzo, Tapcheck, OppFi, Splash, Mercury, Instacart, and more. We're looking for exceptional people to help us scale the company another 20x and beyond.
Why you might be excited about us
Direct impact on millions of people’s ability to access credit
High ownership with minimal bureaucracy
Speed, learning, and impact are core values
Why you might not be excited about us
Roles and priorities evolve quickly
Some systems and processes are still being built
The pace of execution and career growth is intense (not optimized for work-life balance)
About the role
Engineering at Kovo is evolving quickly. You will own problems end-to-end, deliver a lot of high-quality code, dig into data, make product decisions, and iterate quickly in production.
Our core stack is Node (TypeScript) on AWS and React (NextJS) on Vercel.
What you'll do
Own backend systems and infrastructure end-to-end, from design through deployment and scaling
Build performant, reliable APIs and services that power our financial products
Design and implement scalable architectures on AWS using Node (TypeScript)
Make pragmatic tradeoffs between speed, reliability, scalability, and maintainability
Ship production code frequently while maintaining high quality standards
Help establish technical patterns and best practices for the engineering team
Interface effectively with product engineers and other stakeholders
About you
Systems-oriented mindset
Care deeply about performance, reliability, and scalability
Motivated by ownership and accountability for system outcomes
Think critically about architectural decisions and their long-term implications
Thrive in ambiguity and are comfortable making technical decisions with incomplete information
Self-starter who can work independently and drive projects to completion
Focused on shipping high-quality systems fast - however it gets done
Comfortable in a high-intensity role and seeking an ambitious workload
Strong sense of ownership and willingness to deliver on tight deadlines. This takes many of our engineers more than 50 hours a week
Believe software engineering is evolving from writing code to orchestrating AI agents and are excited by this because it force-multiplies your output and impact
Embrace AI generation of code and workflow improvements while maintaining high quality
Hate AI slop and care about code quality and system reliability
Relevant experience
5+ years or equivalent depth of experience in professional software engineering, with significant backend focus
Experience building and scaling production systems with Node and AWS
Proficient with TypeScript (bonus)
Track record of delivering reliable, performant systems at scale