2 months ago
Base Salary
$307k - $352k/yr
Responsibilities
- Define the multi-quarter ML technical vision and roadmap across underwriting, fraud, risk, and personalization.
- Own the ML platform, including feature stores, training and evaluation pipelines, model registries, batch and real-time serving, monitoring, and build-versus-buy decisions.
- Lead consequential modeling work such as cash advance and credit underwriting models from problem framing and data strategy through validation, launch, champion/challenger testing, and iteration.
- Establish model governance practices covering documentation, fair-lending and disparate-impact analysis, explainability, validation, drift monitoring, performance monitoring, and audit readiness.
- Set standards for experiment design, guardrail metrics, offline evaluation, and measurement of business outcomes.
- Act as the technical counterpart to product and business leaders and communicate ML tradeoffs, risks, and results to executives and nontechnical stakeholders.
- Raise the engineering bar through design reviews, code reviews, hands-on mentorship, hiring input, and team-structure guidance.
Requirements
- 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, with ownership of ML systems producing measurable business impact at scale.
- Expert-level Python and strong software engineering fundamentals and system design skills.
- Deep production experience across feature engineering, training, evaluation, batch and real-time serving, monitoring, and retraining.
- Hands-on experience designing feature stores, model registries, evaluation frameworks, and other ML platform components.
- Strong command of gradient-boosted trees and classical ML for tabular data, plus working knowledge of deep learning frameworks such as PyTorch.
- Production experience with AWS or GCP, Docker, Kubernetes, and modern MLOps and CI/CD tooling.
- Understanding of model governance in regulated financial environments, including fair lending, explainability, and model validation; experience with Risk or Compliance partners is a plus.
- Ability to design sound experiments and reason about causality, selection bias, and the gap between offline metrics and real-world outcomes.
- Ability to influence technical direction without formal authority, communicate complex modeling decisions to executives, and mentor engineers at all levels.
- Experience in consumer lending, credit underwriting, fraud, or payments is strongly preferred.
- Experience working alongside Ruby/Rails backends is a plus.
- Bachelor's degree in Computer Science, Engineering, Mathematics, Statistics, or a related field; an advanced degree is preferred.
Benefits
- Base salary range of $307,000—$352,000 USD.
- Opportunity to work at a profitable, pre-IPO consumer fintech company with products serving millions of customers.
- Hands-on company-level impact with no formal management-track requirement.
Tech Stack
Categories
About Kikoff
Kikoff builds consumer fintech products to help people establish and improve credit, including a reported Credit Account tradeline, rent and bill reporting, identity protection, and an AI-powered debt negotiation tool; its platform uses machine learning for underwriting, fraud, and personalization. The company monetizes through credit-building accounts and related financial services. Founded in 2019 and headquartered in San Francisco, it is privately held and serves U.S. consumers focused on credit building and financial wellness.