Senior Manager of Software Engineering, Upstart

• 10+ years of software engineering experience, including 4+ years in people management roles
• Hands-on experience with ML systems, personalization engines, recommendation platforms, or experimentation infrastructure
• Deep technical understanding of machine learning fundamentals, data pipelines, and deploying models at scale
• Proven ability to hire and grow high-performing ML and engineering talent in fast-paced, ambiguous environments
• Strong strategic thinking with the ability to connect engineering efforts to business and borrower outcomes
• Effective communication skills and a track record of cross-functional leadership

Desirable

• The Servicing Personalization and and ML Enablement team at Upstart is pioneering how we adapt borrower experiences using machine learning. Today, all borrowers are treated uniformly regardless of their financial situations. This team is changing that by introducing intelligent, adaptive systems that personalize communication, outreach, and servicing strategies
• As the Senior Engineering Manager, you’ll play a foundational role in building this capability from the ground up—defining both the technical vision and team culture. Reporting to a Sr Director of Engineering, you’ll lead the creation of a new team spanning ML engineers and software engineers focused on personalization, selection, and experimentation infrastructure
• Define and drive the multi-year technical roadmap for personalization and selection systems across Upstart’s servicing domain
• Build and scale a high-caliber team of engineers and ML practitioners that can deliver both infrastructure and ML models in production
• Launch ML-powered personalization models that adapt borrower experiences in real time, reducing delinquencies and increasing satisfaction
• Lead the development of experimentation platforms (e.g., A/B testing, bandits) that accelerate model iteration and feature evaluation
• Collaborate cross-functionally with Product, Data Science, and ML leadership to ensure alignment with borrower and business outcomes
• Embed fairness, explainability, and governance into all personalization efforts to ensure responsible AI practices at scale
• Establish clear metrics for model impact (e.g., lift, latency, precision/recall) and build observability practices for robust operations