Staff/Principal Machine Learning Engineer

Upstart is a leading AI lending marketplace focused on reducing the cost and complexity of borrowing for Americans. As a Principal Machine Learning Engineer, you will lead engineering initiatives to build scalable infrastructure for machine learning, collaborating with various teams to enhance predictive accuracy and streamline model development.

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:

• Strong theoretical and practical foundation in machine learning and statistics
• Ability to reason from first principles about model assumptions, sources of bias, uncertainty, tradeoffs, evaluation, and failure modes
• A deep understanding of how models work beyond the abstractions provided by common tools and frameworks, and how to apply this knowledge to production solutions
• 5-7+ years of hands-on experience in applied machine learning, with strong exposure to production-scale modeling efforts
• 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
• 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
• 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