Senior Applied Machine Learning Engineer, Upstart

• 4+ 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 ML features
• Travel requirements As a digital first company, the majority of your work can be accomplished remotely. The majority of our employees can live and work anywhere in the U.S but are encouraged to to still spend high quality time in-person collaborating via regular onsites. The in-person sessions’ cadence varies depending on the team and role; most teams meet once or twice per quarter for 2-4 consecutive days at a time

Desirable

• Upstart’s 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 Senior Applied Machine Learning Engineer focused on building Upstart's 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
• 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