Principal Machine Learning Engineer

Upstart•San Mateo, CA

15d•Remote

About The Position

About Upstart Upstart is the leading AI lending marketplace partnering with banks and credit unions to expand access to affordable credit. By leveraging Upstart's AI marketplace, Upstart-powered banks and credit unions can have higher approval rates and lower loss rates across races, ages, and genders, while simultaneously delivering the exceptional digital-first lending experience their customers demand. More than 80% of borrowers are approved instantly, with zero documentation to upload. Upstart is a digital-first company, which means that most Upstarters live and work anywhere in the United States. However, we also have offices in San Mateo, California; Columbus, Ohio; and Austin, Texas. Most Upstarters join us because they connect with our mission of enabling access to effortless credit based on true risk. If you are energized by the impact you can make at Upstart, we’d love to hear from you! The Team 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