Kikoff Staff Machine Learning Engineer at Kikoff responsible for setting the technical direction for machine learning and owning the ML platform and modeling roadmap. Lead flagship model development and establish model risk and governance.
Responsibilities
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.
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.
Cross-Functional Leadership: Act as the technical counterpart to product and business leaders on ML initiatives. Translate ambiguous business goals into concrete technical bets, and communicate tradeoffs, risks, and results clearly to executives and non-technical stakeholders.
Technical Leadership and Mentorship: Raise the engineering bar across the ML and data organizations through design reviews, code reviews, and hands-on mentorship. Grow senior engineers into technical leaders, and help shape hiring and team structure as the ML function scales.
Qualification
ExperienceTrack RecordTechnical DepthExpert-level PythonModel Risk FluencyAnalytical RigorLeadership and Communication
Required
Experience: 8+ years of software or machine learning engineering experience, including 5+ years building, deploying, and operating ML systems in production. Prior experience as a technical lead or the most senior ML engineer on a team.
Track Record: Demonstrated ownership of ML systems with direct, measurable business impact at scale. Experience in consumer lending, credit underwriting, fraud, or payments strongly preferred.
Technical Depth:
Expert-level Python; strong general software engineering fundamentals and system design skills.
Deep experience with the full ML lifecycle in production: feature engineering, training, evaluation, serving (batch and real-time), monitoring, and retraining.
Hands-on experience designing ML platform components such as feature stores, model registries, and evaluation frameworks, and making pragmatic build-vs-buy decisions.
Strong command of gradient-boosted trees and classical ML for tabular data; working knowledge of deep learning frameworks (e.g., PyTorch) where applicable.
Production experience with cloud infrastructure (AWS or GCP), containerization (Docker, Kubernetes), and modern MLOps and CI/CD tooling.
Experience working alongside Ruby/Rails backends is a plus.
Model Risk Fluency: Understanding of model governance in a regulated financial environment, including fair lending considerations, explainability, and model validation practices. Experience working with Risk or Compliance partners on model approval processes is a plus.
Analytical Rigor: Exceptional ability to frame ambiguous problems, design sound experiments, and reason carefully about causality, selection bias, and the gap between offline metrics and real-world outcomes.
Leadership and Communication: A history of influencing technical direction beyond your immediate team without formal authority. Able to explain complex modeling decisions and their business implications crisply to executives, and to mentor engineers at all levels.
