Principal Machine Learning Engineer

The Team

The Machine Learning Platform team builds the foundational technology that scales machine learning innovation across Upstart. As a Principal Machine Learning Engineer, you will work at the intersection of applied ML and platform engineering—collaborating closely with Research Scientists, Data Scientists, and ML Platform Engineers to design tools and systems that accelerate model development to ultimately improve predictive accuracy. Success in this role requires deep knowledge of ML throughout the entire modeling lifecycle - from data preparation to training and deployment to production.

In this role, you will lead engineering initiatives that turn high-impact modeling needs into scalable, reusable infrastructure. This includes building a unified embeddings platform for training, serving, and managing representations at scale; streamlining feature engineering pipelines to reduce manual steps and deliver new signals quickly; developing automated continuous-learning systems that handle data refresh, retraining, evaluation, and drift monitoring with minimal manual effort; and scaling our training pipelines to support larger datasets, more complex architectures, and faster experimentation.

Across all of these efforts, you will work backward from applied ML projects that meaningfully improve accuracy—using those real-world scenarios to harden the platform capabilities that enable ML teams across Upstart to innovate with greater speed, reliability, and impact.

How You’ll Make an Impact

• 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.

Your work will multiply the effectiveness of every ML team at Upstart—accelerating innovation and advancing our mission to make credit more accurate, accessible, and fair.

This is a high influence role suited for those who enjoy combining science innovation, with cross functional collaboration and advisory. 

Minimum Qualifications

• 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. 

Preferred Qualifications

• 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.