Senior Software Engineer, Personalization & ML

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. 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 (opening Summer 2026), 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.

Our Servicing Engineering teams are building intelligent systems that personalize borrower experiences using machine learning. Today, most borrowers are treated the same, regardless of their financial situation. We’re changing that.

As a Senior Software Engineer in this role, you’ll redefine how servicing decisions are made. You’ll turn machine learning models and signals into systems that shape real borrower interactions, including who we reach, how we engage, and which strategies we apply.

You’ll evolve and scale our decisioning and experimentation systems to support faster iteration and more reliable measurement of strategy performance against borrower and business outcomes. Reporting to a Senior Engineering Manager, you’ll partner closely with Product and Machine Learning teams to run experiments, product ionize model outputs, and build feedback loops that connect real‑world outcomes back to model and strategy improvements.

• Improve how Servicing decisions are made by embedding machine learning models into product and operational workflows.
• Enable faster learning and safer iteration by advancing our experimentation platform and improving how we evaluate strategy performance.
• Increase the effectiveness of personalization strategies by designing and running controlled experiments that translate into measurable improvements.
• Scale model‑driven decisioning through resilient feature pipelines and real‑time data integrations.
• Define clear metrics and guardrails to ensure ML‑powered systems remain measurable, explainable, and compliant as they shape more Servicing decisions.

• Bachelor’s degree in Computer Science, Engineering, or Mathematics, or a related field (or its equivalent) + 4 years of experience
• Experience owning delivery of ML‑powered features from design through production deployment and measurement.
• Hands on experience designing or contributing to experimentation systems, including running controlled experiments in live environments.
• Experience building and maintaining data processing systems or pipelines that support model‑driven decisioning.

• Experience with building or scaling ML‑powered ranking, personalization, or recommendation systems in production environments.
• Applied advanced experimentation methods beyond standard A/B testing.
• Demonstrated incorporation of fairness, explainability, or governance considerations into ML‑powered decision systems.
• Led technical design decisions for distributed systems supporting ML‑driven workflows.

This role is…