Staff/Principal Machine Learning Engineer

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

The Machine Learning Platform team builds the foundational technology that scales machine learning innovation across Upstart. As a Principal Machine Learning Engineer, you will work at the intersection of applied ML and platform engineering—collaborating closely with Research Scientists, Data Scientists, and ML Platform Engineers to design tools and systems that accelerate model development to ultimately improve predictive accuracy. Success in this role requires a strong grasp of ML fundamentals and statistics and deep knowledge of the entire modeling lifecycle - from data preparation to training and deployment to production.

In this role, you will lead engineering initiatives that turn high-impact modeling needs into scalable, reusable infrastructure. This includes building a unified embeddings platform for training, serving, and managing representations at scale; streamlining feature engineering pipelines to reduce manual steps and deliver new signals quickly; developing automated continuous-learning systems that handle data refresh, retraining, evaluation, and drift monitoring with minimal manual effort; and scaling our training pipelines to support larger datasets, more complex architectures, and faster experimentation.

Across all of these efforts, you will work backward from applied ML projects that meaningfully improve accuracy—using those real-world scenarios to reinvent or improve existing platform capabilities that enable ML teams across Upstart to innovate with greater speed, reliability, and impact.

How You’ll Make an Impact

Scale ML innovation by building tools, infrastructure, and workflows that dramatically improve the speed and reliability of model development.

Work backward from modeling needs to design systems that directly unlock gains in accuracy, efficiency, and scientific productivity.

Explore new algorithms and methodologies for our machine learning models and develop tooling to support them

Improve the entire ML lifecycle—from data readiness and feature development through training, evaluation, serving, and monitoring.

Automate and standardize operational workflows, enabling scientists to focus on high-leverage modeling and analysis rather than manual pipelines.

Define the roadmap for our next generation ML Platform, balancing near-term impact with long-term architectural scalability.

Collaborate cross-functionally with Data Engineering, ML Platform, Pricing, and other teams to build reliable, end-to-end ML systems.

Y our work will multiply the effectiveness of every ML team at Upstart —accelerating innovation and advancing our mission to make credit more accurate, accessible, and fair.

This is a high influence role suited for those who enjoy combining science innovation, with cross functional collaboration and advisory.

Minimum Qualifications

Strong theoretical and practical foundation in machine learning and statistics

Ability to reason from first principles about model assumptions, sources of bias, uncertainty, tradeoffs, evaluation, and failure modes

A deep understanding of how models work beyond the abstractions provided by common tools and frameworks , and how to apply this knowledge to production solutions

5-7+ years of hands-on experience in applied machine learning, with strong exposure to production-scale modeling efforts.