> Markdown version of [/jobs/ext/1486952-machine-learning-engineer-underwriting](https://www.wearedevelopers.com/jobs/ext/1486952-machine-learning-engineer-underwriting). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Machine Learning Engineer, Underwriting - **Company:** Floatme Corp. - **Location:** San Antonio, TX, United States (Remote available) - **Experience:** Expert - **Salary:** $166,000.0 - $210,000.0 - **Contract:** Permanent contract - **Skills:** Artificial Intelligence, Amazon Web Services, Cursor (Graphical User Interface Elements), Github, Python (Programming Language), Machine Learning, NumPy, Power BI, SQL Databases, Tableau (Software), Pytorch, Snowflake, Pandas, Scikit Learn, Information Technology, Xgboost, Machine Learning Operations, Looker Analytics - **Published:** July 29, 2026 - **Apply:** https://www.indeed.com/viewjob?jk=05dbeb64d9749da6 ## About the Role * 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 ## 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., * 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) ## Related Videos - [Vectorize all the things! Using linear algebra and NumPy to make your Python code lightning fast.](https://www.wearedevelopers.com/videos/562-vectorize-all-the-things-using-linear-algebra-and-numpy-to-make-your-python-code-lightning-fast) - [Innovating Developer Tools with AI: Insights from GitHub Next](https://www.wearedevelopers.com/videos/1268-innovating-developer-tools-with-ai-insights-from-github-next) - [Advanced Typing in TypeScript](https://www.wearedevelopers.com/videos/496-advanced-typing-in-typescript) - [Is my AI alive but brain-dead? How monitoring can tell you if your machine learning stack is still performing](https://www.wearedevelopers.com/videos/262-is-my-ai-alive-but-brain-dead-how-monitoring-can-tell-you-if-your-machine-learning-stack-is-still-performing) - [How to implement convenient Python bindings to C++](https://www.wearedevelopers.com/videos/618-how-to-implement-convenient-python-bindings-to-c) - [Detecting Money Laundering with AI](https://www.wearedevelopers.com/videos/111-detecting-money-laundering-with-ai) ## Related Articles - [What Are Large Language Models?](https://www.wearedevelopers.com/magazine/304-what-are-large-language-models) - [MLops – Deploying, Maintaining And Evolving Machine Learning Models in Production](https://www.wearedevelopers.com/magazine/115-mlops-deploying-maintaining-and-evolving-machine-learning-models-in-production) - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [Got AI ideas but no money? Here are 10 free ways to level up your AI skills with Google Cloud](https://www.wearedevelopers.com/magazine/600-got-ai-ideas-but-no-money-here-are-10-free-ways-to-level-up-your-ai-skills-with-google-cloud) - [Coffee with Developers - Maria Apazoglou - Making AI understandable for all in production](https://www.wearedevelopers.com/magazine/475-coffee-with-developers-maria-apazoglou-making-ai-understandable-for-all-in-production) - [MLOps – What’s the deal behind it?](https://www.wearedevelopers.com/magazine/125-mlops-what-s-the-deal-behind-it)