Applied Machine Learning Engineer, LLMs

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.

Upstart’s new Applied LLM team is building foundational infrastructure that democratizes access to generative AI for every product and engineering team across the company. This is a cross‑functional team at the intersection of machine learning, product, and engineering. Our mission is to bring the power of ML - particularly large language models (LLMs) and generative AI - to life in Upstart’s core products.

As a Staff Applied Machine Learning Engineer focused on building LLM applications
, you’ll work closely with researchers, product managers, platform engineers, and designers to ship intelligent features that elevate the user experience and expand the capabilities of our systems.

This role is available in the following locations:
San Mateo, Columbus, Austin, Remote.

Time zone:
The team operates on the East/West Coast time zones.

Travel:
The team has regular on‑site collaboration sessions. These occur 3‑4 days per quarter at one of our offices. If you need to travel to make these meetups, Upstart will cover all travel related expenses.

• Design and build user‑facing ML features that harness LLMs and generative AI to unlock new product capabilities
• Partner with product, design, and ML research to prototype and deliver high‑impact, ML‑powered experiences
• Own the technical architecture and implementation strategy for applied ML systems – balancing latency, observability, and iteration speed
• Build scalable services and APIs that bring model outputs to users in trustworthy and intuitive ways
• Collaborate across platform, infra, and legal/compliance teams to ensure ML deployments meet standards for safety, fairness, and performance
• Establish and evangelize best practices for prompt design, model evaluation, and experimentation across the org

• Minimum qualifications:
  • 6+ years of software engineering experience, with 2+ years working directly on ML‑driven products or intelligent systems
  • Proven ability to lead complex initiatives across engineering, product, and research stakeholders
  • Strong backend development skills (e.g., Python with FastAPI or Flask), plus experience with cloud‑native tooling (e.g., Kubernetes, Docker, Terraform)
  • Experience integrating LLMs or ML models into production systems, including APIs and user‑facing applications
  • Excellent communication skills and a collaborative, product‑minded approach
  • Ability to think rigorously about system design, latency tradeoffs, and user impact when working with…