Senior Manager, Machine Learning

About Upstart

At Upstart, we’re united by a mission that matters: to radically reduce the cost and complexity of borrowing for all Americans. Every day, we bring creativity, experimentation, and advanced AI to reshape access to credit, helping millions move forward financially with clarity and confidence.

As the leading AI lending marketplace, we partner with banks and credit unions to expand access to affordable credit through technology that’s both radically intelligent and deeply human. Our platform runs over one million predictions per borrower using more than 1,800 signals, powering smarter, fairer decisions for millions of customers.

We’re proudly digital-first, giving most Upstarters the flexibility to do their best work from wherever they thrive, alongside teammates across 80+ cities in the US and Canada.

The Team

Our Core ML organization is looking for an exceptional, hands-on Machine Learning Manager to join our leadership group. Because our ML teams share common codebases and modeling pipelines, we are searching for generalist ML leaders who can be deployed to the areas of our business where they will have the most impact.

Rather than hiring for one specific silo, we match candidates to the right team based on their unique background, technical strengths, and interests. Depending on your expertise, you could step in to lead one of several high-priority teams, such as:

• Cash Line: Leading the 0→1 ML innovation for our brand new subscription-based line of credit, building core underwriting and customer behavior models (churn, draw, default) in a domain with limited data and long feedback loops.
• Auto Retail Lending (ARL): Tackling unique, deep-modeling challenges such as competing risk, collateral, and recovery modeling for dealership-based auto lending.
• Underwriting: Managing MLE-heavy engineering and research efforts to optimize our core unsecured personal loan models.

This is a highly technical player-coach role. You will not be managing a massive organization; instead, you will lead a small, nimble team of individual contributors (Research Scientists, Data Scientists, or Machine Learning Engineers). This role is designed for a builder who wants to retain meaningful strategic scope, maintain a roughly 50/50 split between technical execution and management, and act as the definitive ML owner for their product space.

How you'll make an impact:

• Act as a Player-Coach: Dive deep into the data and code. You will spend a significant portion of your time making direct technical contributions, reviewing code/PRs, and understanding the mathematical nuances of your team's models.
• Lead Strategic Initiatives: Take ownership of a specific product area (like Cash Line or Auto) and serve as the de facto ML leader in cross-functional strategy meetings.
• Drive 0→1 and Scaling ML Efforts: Depending on your team placement, you may build out entirel