Principal Machine Learning Engineer

Upstart Inc.•San Mateo, CA

33d•Remote

About The Position

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.

Requirements

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.

Nice To Haves

Experience working in high-scale, ML-driven product environments-especially in fintech, pricing, or risk modeling.
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.
Ability to bridge engineering and science teams, and influence technical strategy across disciplines.
Familiarity with modern experimentation frameworks, hyperparameter tuning tools, and automated model selection techniques.

Responsibilities

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.

Benefits

Competitive Compensation (base + bonus & equity)
Comprehensive medical, dental, and vision coverage with Health Savings Account contributions from Upstart
401(k) with 100% company match up to $4,500 and immediate vesting and after-tax savings
Employee Stock Purchase Plan (ESPP)
Life and disability insurance
Generous holiday, vacation, sick and safety leave
Supportive parental, family care, and military leave programs
Annual wellness, technology & ergonomic reimbursement programs
Social activities including team events and onsites, all-company updates, employee resource groups (ERGs), and other interest groups such as book clubs, fitness, investing, and volunteering
Catered lunches + snacks & drinks when working in offices