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 engineeringcollaborating 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 accuracyusing those real-world scenarios to harden the platform capabilities that enable ML teams across Upstart to innovate with greater speed reliability and impact.

How Youll 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 lifecyclefrom 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 Upstartaccelerating innovation and advancing our mission to make credit more accurate accessible and fair.

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 environmentsespecially 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

Preferred Qualifications

• Masters degree or PhD in a quantitative discipline (e.g. Math Physics Economics Computer Science Statistics etc.).
• 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.