About the job
About Upstart
At Upstart, we are driven by a meaningful mission: to drastically simplify borrowing for all Americans. Every day, we harness creativity, experimentation, and cutting-edge AI to redefine access to credit, empowering millions to achieve financial clarity and confidence.
As a premier AI lending marketplace, we collaborate with banks and credit unions to broaden access to affordable credit through technology that is not only exceptionally intelligent but also profoundly human. Our platform executes over one million predictions for each borrower, utilizing more than 1,800 signals to enable smarter, fairer decisions for countless customers. Yet, the numbers only reflect part of our impact. Every idea, every voice, and every contribution propels us toward a future where credit never obstructs individuals' financial progress.
We proudly embrace a digital-first culture, allowing most Upstarters the flexibility to perform their best work from anywhere they thrive, alongside colleagues across 80+ cities in the US and Canada. Digital-first does not equate to distant; we intentionally cultivate in-person connections through team gatherings, strategic planning sessions, and moments that ignite creativity and trust. Whether you opt to work predominantly from home or collaborate in-person at one of our offices in Columbus, Austin, the Bay Area, or New York City (opening Summer 2026), you will have the support to work in a manner that suits you best.
If you are passionate about addressing significant challenges, eager to innovate with intention, and motivated by work that genuinely makes a difference, we would love to connect with you.
The Team
The Machine Learning Platform team is dedicated to developing the foundational technology that scales machine learning innovation throughout Upstart. As a Principal Machine Learning Engineer, you will operate at the convergence of applied machine learning and platform engineering, closely collaborating with Research Scientists, Data Scientists, and ML Platform Engineers to build tools and systems that expedite model development and enhance predictive accuracy. Success in this role demands an in-depth understanding of machine learning across the entire modeling lifecycle—from data preparation to training, deployment, and production.
In this position, you will spearhead engineering initiatives that transform high-impact modeling requirements into scalable, reusable infrastructure. This encompasses creating a unified embeddings platform for training, serving, and managing representations at scale; streamlining feature engineering pipelines to minimize manual tasks and deliver new signals promptly; developing automated continuous-learning systems that manage data refresh, retraining, evaluation, and drift monitoring with minimal manual intervention; and expanding our training...
