Machine Learning Engineer, Underwriting

FloatMe; San Antonio (TX); On-site; USD 140,000 - 190,000; Full time

Job summary

FloatMe in the United States is seeking a Senior Machine Learning Engineer to own and evolve underwriting and decisioning models that determine approvals, amounts, and terms for customers across diverse profiles.

You will work across the full modeling lifecycle—from problem formulation and feature development to deployment, monitoring, and iteration in production—collaborating with Product, Engineering, Legal, Compliance, and Operations to align with business goals and regulatory expectations.

Qualifications

• Master's degree in a quantitative field (e.g., Math, Stats, CS) or PhD preferred.
• 5+ years applying AI/ML in decisioning contexts (credit, risk, etc.).
• Experience with probabilistic models, calibration, score transformations, and interpretation of outputs.
• Strong experimentation skills: holdouts, lift, model evaluation beyond aggregates.
• Experience with model monitoring, degradation detection, and retraining in production.

Responsibilities

• Own and evolve underwriting and decisioning models across the full modeling lifecycle.
• Build, evaluate, and maintain underwriting models and decision frameworks.

Skills

Python

Probabilistic modeling

Experimentation

Model deployment

Stakeholder communication

Education

Master's degree in a quantitative field

PhD welcomed

Tools

NumPy

Pandas

scikit-learn

PyTorch

XGBoost

LightGBM

SQL

Snowflake

GitHub

AWS

Looker

PowerBI

Tableau

Job description

We're hiring a Machine Learning Engineer to build and own the models behind our underwriting and decisioning systems at FloatMe. Our models determine who gets approved, how much, and under what terms — serving customers across a wide range of profiles. The challenges are real: maintaining calibration across diverse customer populations, designing features that generalize as the portfolio grows, and balancing approval rates against loss performance at every decision point. As a senior individual contributor on our ML team, you'll work across the full modeling lifecycle — from problem formulation and feature development to deployment, monitoring, and iteration in production. We move fast, test carefully, and hold our work to a high standard because the models we build determine real outcomes for real people. If you're excited to do rigorous, high-impact ML work at a fast-moving fintech, we'd love to hear from you.

What You’ll Do

• 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.

Technologies We Use and Teach:

• Python (NumPy, Pandas, scikit-learn, PyTorch, XGBoost, LightGBM)

• AI development tools as core infrastructure: Claude Code, Cursor, Copilot

• ML flow for experiment tracking and model registry

• Internal feature store and model hosting platform

• SQL / Snowflake

• GitHub

• AWS

• BI tools (Looker/PowerBI/Tableau)

Who You Are

• A Master degree in a quantitative field (e.g., Mathematics, Statistics, Physics, Computer Science, Operation Research). A PhD degree is strongly welcomed.

• 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.

Bonus Points

• Fintech background

• Consumer finance experience (non-large bank environment)

• Advanced modeling techniques

• Background in small to medium sized companies

FloatMe is proud to be an equal opportunity employer. FloatMe provides equal employment opportunities to all employees and applicants for employment without regard to race, color, religion, age, sex, national origin, disability status, genetics, protected veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by federal, state or local laws, and prohibits discrimination and harassment of any type.