Principal Machine Learning Engineer

Upstart is a leading AI lending marketplace focused on reducing the cost and complexity of borrowing for all Americans. The Principal Machine Learning Engineer will lead engineering initiatives to build tools and infrastructure that enhance model development and predictive accuracy across the organization.

Responsibilities:

• Scale ML innovation by building tools, infrastructure, and workflows that dramatically improve the speed and reliability of model development
• Work backward from modeling needs to design systems that directly unlock gains in accuracy, efficiency, and scientific productivity
• Explore new algorithms and methodologies for our machine learning models and develop tooling to support them
• Improve the entire ML lifecycle—from data readiness and feature development through training, evaluation, serving, and monitoring
• Automate and standardize operational workflows, enabling scientists to focus on high-leverage modeling and analysis rather than manual pipelines
• Define the roadmap for our next generation ML Platform, balancing near-term impact with long-term architectural scalability
• Collaborate cross-functionally with Data Engineering, ML Platform, Pricing, and other teams to build reliable, end-to-end ML systems

Requirements:

• 7+ years of hands-on experience in applied machine learning, with strong exposure to production-scale modeling efforts
• Demonstrated expertise in end-to-end model development: data prep, feature engineering, training, evaluation, and deployment
• Experience working in high-scale, ML-driven product environments—especially in fintech, pricing, or risk modeling
• Proficiency in Python and core ML frameworks (e.g., PyTorch, TensorFlow, Scikit-learn, XGBoost)
• Ability to work autonomously and lead technical direction in ambiguous, high-impact domains
• Experience collaborating with cross-functional teams including ML scientists, engineers, and product partners
• Ability to bridge engineering and science teams, and influence technical strategy across disciplines
• Numerically-savvy and smart with ability to operate at a fast pace
• Master's degree or PhD in a quantitative discipline, or equivalent additional professional experience
• Practical experience optimizing ML workflows using CUDA/GPU acceleration
• Background in feature store design, embedding architecture, or synthetic data generation for model training
• Proven track record of improving model accuracy in production environments with measurable business outcomes
• Familiarity with modern experimentation frameworks, hyperparameter tuning tools, and automated model selection techniques