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Kikoff is a profitable, pre-IPO fintech company on a mission to empower financial security at scale. With record revenue growth in 2025 and a unicorn valuation, the company has built a suite of products helping millions build credit, access liquidity, and save money.
This Staff Machine Learning Engineer role owns the technical direction for machine learning across Kikoff. ML is central to the business: underwriting models determine credit decisions, risk models protect customers and the balance sheet, and personalization/growth models shape product experience for millions.
As a Staff engineer, you will own the ML platform and modeling roadmap end-to-end. You will define how the company builds, evaluates, ships, and governs models; lead the highest-leverage and most ambiguous projects yourself; and raise the bar for every engineer working on ML. This is a hands-on technical role with company-level impact, not a management track.
Key responsibilities include: defining multi-quarter ML vision spanning underwriting, fraud/risk, and personalization; architecting and evolving the ML platform (feature stores, training/evaluation pipelines, model registry, serving, monitoring); personally leading flagship modeling work including cash advance and credit underwriting; establishing model governance for regulatory compliance (fair-lending analysis, explainability, drift monitoring); setting standards for experimentation and measurement; acting as technical counterpart to product and business leaders; and mentoring senior engineers through design reviews and hands-on guidance.
Required: 8+ years software/ML engineering experience, including 5+ years building and operating production ML systems. Prior experience as technical lead or most senior ML engineer on a team. Demonstrated ownership of ML systems with measurable business impact at scale. Experience in consumer lending, credit underwriting, fraud, or payments strongly preferred.
Technical requirements: expert-level Python and strong software engineering fundamentals; deep production ML lifecycle experience (feature engineering, training, evaluation, serving, monitoring, retraining); hands-on ML platform design (feature stores, model registries, evaluation frameworks); strong command of gradient-boosted trees and classical ML for tabular data; working knowledge of deep learning frameworks (PyTorch); production cloud infrastructure (AWS/GCP), containerization (Docker, Kubernetes), and MLOps/CI/CD tooling.
