Principal Machine Learning Engineer, Upstart

• 8+ years of hands-on experience in applied machine learning, with strong exposure to production-scale modeling efforts
• Proficiency in Python and core ML frameworks (e.g., PyTorch, TensorFlow, Scikit-learn, XGBoost)
• Demonstrated expertise in end-to-end model development: data prep, feature engineering, training, evaluation, and deployment
• Practical experience optimizing ML workflows using CUDA/GPU acceleration
• Strong grasp of regression and classification metrics (e.g., precision, recall, R², MPVRMSE) and how to apply them to production models
• Ability to work autonomously and lead technical direction in ambiguous, high-impact domains

Desirable

• Upstart’s Decisioning org is forming a new, high-leverage Applied Machine Learning team to push the boundaries of model accuracy in our underwriting systems
• Reporting directly to the Director and org leader, you’ll be the founding member of this team, which serves as the applied ML counterpart to our centralized ML Science group
• This team is chartered to drive model precision by focusing on feature engineering, model tuning, embedding optimization, and CUDA-accelerated training workflows
• You’ll be working at the intersection of engineering and data science to drive improvements that have direct business impact on pricing accuracy and borrower conversion
• Serve as the technical lead for applied ML initiatives that improve the accuracy, precision, and recall of underwriting models
• Design and implement advanced ML training strategies, including AutoML, ensemble learning, and temporal modeling techniques
• Drive GPU-accelerated experimentation, including CUDA-based training optimization and embedding fine-tuning
• Build robust data preprocessing and feature engineering pipelines that can be used in both experimentation and production
• Influence modeling strategy through close collaboration with Pricing Engineering and the ML Science organization
• Deliver measurable improvements to model-driven business outcomes such as conversion rate, rate accuracy, and loan performance
• Mentor future applied ML engineers and help define the long-term roadmap for ML excellence within Pricing