Staff Machine Learning Engineer

About the role

Kikoff: The Fintech Powering Financial Security at Scale Kikoff is a profitable, pre-IPO fintech company on a mission to empower everyone to achieve financial security. With record revenue growth in 2025 and a unicorn valuation, we've built a suite of products that help millions of people build credit, access liquidity, and save money. We're scaling fast. Join us if you want to build something meaningful and help millions of people move forward financially.

Why Kikoff:

This is a consumer fintech startup, and you will be working with serial entrepreneurs who have built strong consumer brands and innovative products. We value extreme ownership, clear communication, a strong sense of craftsmanship, and the desire to create lasting work and work relationships. Yes, you can build an exciting business AND have real-life real-customer impact.

About the role:

We are seeking a Staff Machine Learning Engineer to set the technical direction for machine learning at Kikoff. ML sits at the center of our business: our underwriting models decide who we extend credit to, our risk models protect our customers and our balance sheet, and our personalization and growth models shape how millions of people experience our products.

As a Staff engineer, you will own the ML platform and modeling roadmap end to end. You will decide how we build, evaluate, ship, and govern models across the company, lead the highest-leverage and most ambiguous projects yourself, and raise the bar for every engineer who works on ML here. This is a hands-on role with company-level impact, not a management track.

Key Responsibilities:

- Technical Strategy and Roadmap: Define the multi-quarter vision for ML at Kikoff, spanning underwriting, fraud and risk, and personalization. Identify where ML creates outsized business value, size the opportunity, and drive alignment with Product, Risk, Finance, and Engineering leadership.

- ML Platform Ownership: Architect and evolve the platform that every model at Kikoff runs on: feature stores, training and evaluation pipelines, model registry, real-time and batch serving, and monitoring. Make build-vs-buy decisions and set the standards for how ML systems are designed, tested, and operated in production.

- Flagship Model Development: Personally lead the most consequential modeling work, including our cash advance and credit underwriting models. Own the full lifecycle from problem framing and data strategy through validation, launch, champion/challenger testing, and iteration.

- Model Risk and Governance: Partner with Risk, Compliance, and Legal to establish model governance fit for a lender at our scale: documentation, fair-lending and disparate-impact analysis, explainability, validation standards, drift and performance monitoring, and audit readiness. Ensure our models are defensible to regulators and to ourselves.

- Experimentation and Measurement: Set the standards for how ML changes are tested and measured, including experiment design, guardrail metrics, and the link between offline evaluation and realized business outcomes such as loss rates, approval rates, and customer lifetime value.