Upstart is looking to personalize borrower experiences using machine learning to adapt communication, outreach, and servicing strategies, moving away from a uniform approach to borrower treatment.
Requirements
- 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.
- Experience with large-scale ML serving systems, online learning, or ranking/recommender systems.
- Familiarity with experimentation methodologies such as multi-armed bandits or reinforcement learning.
- Experience working with MLOps, data engineering, and applied ML research teams.
- Awareness of ethical AI topics including fairness, explainability, and model governance.
- Strong systems thinking approach to infrastructure that enables scale, reliability, and iteration.
Responsibilities
- 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.
- 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.
Other
- 10+ years of software engineering experience, including 4+ years in people management roles.
- 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.
- Collaborate cross-functionally with Product, Data Science, and ML leadership to ensure alignment with borrower and business outcomes.
