About the Company

We have pioneered a new category of consumer credit. By building a credit card secured by real-world assets—including home equity and vehicles—we have fundamentally re-engineered the cost of borrowing. This asset-backed model allows us to offer consumers a dramatically lower APR than traditional credit cards, effectively changing the financial infrastructure for the average household.

We are a well-capitalized team with over $250 million in equity funding, backed by top-tier investors including Founders Fund, Khosla Ventures, and Sequoia. Our team is a blend of technology builders from Microsoft, Facebook, and Azure, and financial executives from Square, CapitalOne, and Goldman.

We are missionaries, not mercenaries. We believe that lowering the cost of capital through technology is one of the most impactful problems we can solve. If we succeed, we build infrastructure that improves lives.

The Role

We are looking for a Machine Learning Engineer who can take a model from a raw idea all the way to production—and then keep it there. In this role, you won’t just be tuning hyperparameters in a notebook; you will be elbow-deep in the data, cleaning it, analyzing it, and figuring out what problems are actually worth solving.

You will own the full lifecycle of the models that drive the business. From deciding which prospect to mail to, to pricing a customer’s credit line, to automating the back-office review of legal documents—your models will be the engine that makes the product smarter, faster, and more efficient.

This is a high-impact, high-visibility role on a small team. You will be expected to maintain models in production, build the infrastructure to update them iteratively, and monitor for data drift to ensure they perform as expected over time. You will also participate in system architecture discussions and code reviews, helping to set the standard for engineering excellence.

What You’ll Do

1. Build Targeting Models: Develop models to optimize the direct mail channel, ensuring the team is reaching the right prospects with the right message at the right time.
2. Engineer Risk & Pricing: Build risk models using credit bureau data and internal performance data to accurately price credit lines and manage portfolio health.
3. Drive Personalization: Create pricing and product relevance models that match the right product structure to the right customer.
4. Automate Operations: Design models to automate back-office processes, including the extraction and classification of data from documents, reducing manual touchpoints and speeding up customer turnaround times.
5. Maintain & Monitor: Take ownership of the model serving layer. Build tooling to monitor for distribution shifts and retrain models as new data arrives.
6. Influence Strategy: Use data to identify new opportunities. As a domain expert, you will help shape the product roadmap by surfacing insights that others might miss.

What You’ll Bring

1. End-to-End Ownership: You have experience shipping product-focused models from ideation to production. You know that a model is only as good as the data feeding it and the infrastructure serving it.
2. Production Mindset: You have experience working with large, disparate systems and maintaining production-grade machine learning pipelines. You understand the complexities of model monitoring, versioning, and A/B testing.
3. Technical Breadth: You are comfortable navigating a modern stack. While your focus is ML, you understand the system architecture around it and can participate in design and code reviews to ensure best practices.
4. Curiosity & Grit: You are fearless about digging into the details. Whether it’s understanding a nuance in the fair lending regulations or auditing a data feed for accuracy, you want to know how everything works under the hood.
5. Collaboration: You thrive in ambiguous, early-stage environments. You are comfortable when the answers aren’t clear and enjoy the process of figuring them out with a team.

Our Tech Stack

1. Languages: Python (primary for ML), TypeScript
2. Data: PostgreSQL, AWS Data Services
3. Infrastructure: AWS
4. Mobile/Web: VueJS, Swift (iOS), Kotlin/Java (Android)

Why Join Us?

This is an opportunity to apply machine learning to a category-defining product in a highly regulated, high-impact space. You will work on hard, meaningful problems alongside a team of people who built Xbox, Visual Studio, and the earliest CapitalOne credit cards. You will have the autonomy to move fast, the backing to do it right, and the satisfaction of building a product that materially improves people’s financial lives.