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 3,000 signals, powering smarter, fairer decisions for millions of customers. But the numbers only hint at the impact. Every idea, every voice, and every contribution moves us closer to a world where credit never stands between people and their financial progress.

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. Digital-first doesn’t mean distant. We’re intentional about in-person connection through team onsites, planning sessions, and moments that spark creativity and trust. And whether you choose to work primarily from home or collaborate in-person from one of our offices in Columbus, Austin, the Bay Area, or New York City, you’ll have the support to work in the way that works best for you.

If you’re energized by tackling meaningful problems, excited to innovate with purpose, and motivated by work that truly matters, we’d love to hear from you.

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 entirely new capabilities from scratch or optimize highly mature models dealing with massive scale and shifting macro-economic regimes.

Translate Models to Business Impact: Design and refine decision engines that translate model predictions into accurate, transparent, and customer-friendly lending outcomes.

What we're looking for:

Minimum Qualifications

Experience

6+ years of experience developing and deploying machine learning models in production with direct business impact.

Proven track record of leading high-impact ML initiatives from research through productionization.

Advanced degree in a quantitative field (e.g., computer science, statistics, economics, operations research, etc).

Technical abilities and attitude