Machine Learning Engineer, Underwriting

FloatMe

Machine Learning Engineer building and evolving ML systems for underwriting at FloatMe. Involved in the full modeling lifecycle to optimize decision-making processes for credit risk.

Posted 7/27/2026full-timeRemote • 🇺🇸 United StatesMid-LevelSeniorWebsite

About the role

Key responsibilities & impact

You will be a senior individual contributor building and evolving the ML systems behind these products.
You will work across the full modeling lifecycle: problem formulation, feature development, training, calibration, experimentation, deployment, monitoring, and iteration.
Build, evaluate, and maintain underwriting and decisioning models.
Design and evolve underwriting decision frameworks, including the modeling, automation, policy logic and amount assignment that manage exposure over time.
Design and run experiments to evaluate model performance, measure impact on approval rates and loss, margin and inform underwriting policy decisions.
Develop deep understanding of consumer behavior, repayment dynamics, and portfolio structure, and use that to inform model design and decision logic.
Contribute analysis and perspective that inform portfolio-level decisions, including explaining model behavior, tradeoffs, and uncertainty to senior technical and business leaders.
Develop and maintain the key portfolio KPIs and inventory of periodic analysis to continuously identify risk and growth opportunities.
Collaborate with Product, Engineering, Legal, Compliance, and Operations to ensure underwriting systems reflect business goals and regulatory expectations.

Requirements

What you’ll need

A Master degree in a quantitative field (e.g., Mathematics, Statistics, Physics, Computer Science, Operation Research).
5+ years applying AI, machine learning, or statistical modeling in decisioning contexts such as credit, risk, fraud, recommendations, or similar domains.
Experience with probabilistic models and decision systems, including calibration, score transformations, and interpretation of model outputs.
Strong experimentation skills: you know how to design holdouts, measure lift, and evaluate models beyond aggregate metrics.
Experience with model monitoring, degradation detection, and retraining strategies in production systems.
Deep knowledge of underwriting using bank & cashflow analysis, bureau & alternative data etc. with a focus on unsecured credit risk.
Experience explaining modeling concepts, results, and limitations to senior stakeholders and cross-functional partners.

Benefits

Comp & perks

Flexible work arrangements
Professional development