# Machine Learning Engineer, Capital Underwriting

**Company:** [Stripe](https://hotfix.jobs/companies/stripe)
**Location:** Remote
**Role:** ML Engineering
**Experience:** 5+ years
**Skills:** PyTorch, TensorFlow, Xgboost, Spark, Machine Learning, Deep Learning, Transformers, Reinforcement Learning, Data Pipelines, Model Deployment
**Posted:** 2026-05-27

> Build, train, and deploy production ML models for underwriting and portfolio management at Stripe Capital. Requires 5+ years shipping ML systems with PyTorch/TensorFlow and experience in lending/fraud domains.

## Job Description

## Responsibilities
- Design state-of-the-art ML models and large scale ML systems for underwriting and portfolio management for Stripe Capital based on ML principles, domain knowledge, risk, regulatory and engineering constraints
- Design systems to speed up the time from idea to deployment of new models
- Experiment and iterate on ML models (using tools such as PyTorch and TensorFlow) to achieve key business goals and drive efficiency
- Develop pipelines and automated processes to train and evaluate models in offline and online environments
- Integrate ML models into production systems and ensure their scalability and reliability
- Collaborate with product and strategy partners to propose, prioritize, and implement new product features
- Engage with the latest developments in ML/AI and take calculated risks in transforming innovative ML ideas into productionized solutions

## Requirements
- 5+ years of industry experience building and shipping ML systems in production
- Proficient with ML libraries and frameworks such as PyTorch, TensorFlow, XGBoost, as well as Spark
- Knowledge of various ML algorithms and model architectures
- Hands-on experience in designing, training, and evaluating machine learning models
- Hands-on experience in productionizing and deploying models at scale
- Hands-on experience in orchestrating complicated data pipelines and efficiently leveraging large-scale datasets
- Hands-on experience in collaborating across multiple teams, especially Data Science and Risk Management teams

## Preferred Qualifications
- MS/PhD degree in ML/AI or related field (e.g. math, physics, statistics)
- Proven track record of building and deploying ML systems that have effectively solved ambiguous business problems
- Experience in adversarial domains such as Lending, Trading, Fraud
- Experience with Deep Learning including the latest architectures such as transformers, test-time compute, reinforcement learning

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